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Kazi A, Mora J, Fischl B, Dalca AV, Aganj I. Multi-Head Graph Convolutional Network for Structural Connectome Classification. Graphs Biomed Image Anal Overlapped Cell Tissue Dataset Histopathol (2023) 2024; 14373:27-36. [PMID: 38665679 PMCID: PMC11044650 DOI: 10.1007/978-3-031-55088-1_3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/28/2024]
Abstract
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, thoroughly capturing representations from the input data. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
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Affiliation(s)
- Anees Kazi
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, USA
- Radiology Department, Harvard Medical School, Boston, USA
| | - Jocelyn Mora
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, USA
- Radiology Department, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, USA
- Radiology Department, Harvard Medical School, Boston, USA
- CSAIL, Massachusetts Institute of Technology, Cambridge, USA
| | - Iman Aganj
- Athinoula A. Martinos Center for Biomedical Imaging, Radiology Department, Massachusetts General Hospital, Boston, USA
- Radiology Department, Harvard Medical School, Boston, USA
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2
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Kelley W, Ngo N, Dalca AV, Fischl B, Zöllei L, Hoffmann M. BOOSTING SKULL-STRIPPING PERFORMANCE FOR PEDIATRIC BRAIN IMAGES. ArXiv 2024:arXiv:2402.16634v1. [PMID: 38463507 PMCID: PMC10925384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Skull-stripping is the removal of background and non-brain anatomical features from brain images. While many skull-stripping tools exist, few target pediatric populations. With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing. However, the broad range of neuroanatomical variation in the developing brain, combined with additional challenges such as high motion levels, as well as shoulder and chest signal in the images, leaves many adult-specific tools ill-suited for pediatric skull-stripping. Building on an existing framework for robust and accurate skull-stripping, we propose developmental SynthStrip (d-SynthStrip), a skull-stripping model tailored to pediatric images. This framework exposes networks to highly variable images synthesized from label maps. Our model substantially outperforms pediatric baselines across scan types and age cohorts. In addition, the <1-minute runtime of our tool compares favorably to the fastest baselines. We distribute our model at https://w3id.org/synthstrip.
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Affiliation(s)
- William Kelley
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Nathan Ngo
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science & Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
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3
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Gazula H, Tregidgo HFJ, Billot B, Balbastre Y, William-Ramirez J, Herisse R, Deden-Binder LJ, Casamitjana A, Melief EJ, Latimer CS, Kilgore MD, Montine M, Robinson E, Blackburn E, Marshall MS, Connors TR, Oakley DH, Frosch MP, Young SI, Van Leemput K, Dalca AV, FIschl B, Mac Donald CL, Keene CD, Hyman BT, Iglesias JE. Machine learning of dissection photographs and surface scanning for quantitative 3D neuropathology. bioRxiv 2024:2023.06.08.544050. [PMID: 37333251 PMCID: PMC10274889 DOI: 10.1101/2023.06.08.544050] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/20/2023]
Abstract
We present open-source tools for 3D analysis of photographs of dissected slices of human brains, which are routinely acquired in brain banks but seldom used for quantitative analysis. Our tools can: (i) 3D reconstruct a volume from the photographs and, optionally, a surface scan; and (ii) produce a high-resolution 3D segmentation into 11 brain regions per hemisphere (22 in total), independently of the slice thickness. Our tools can be used as a substitute for ex vivo magnetic resonance imaging (MRI), which requires access to an MRI scanner, ex vivo scanning expertise, and considerable financial resources. We tested our tools on synthetic and real data from two NIH Alzheimer's Disease Research Centers. The results show that our methodology yields accurate 3D reconstructions, segmentations, and volumetric measurements that are highly correlated to those from MRI. Our method also detects expected differences between post mortem confirmed Alzheimer's disease cases and controls. The tools are available in our widespread neuroimaging suite "FreeSurfer" ( https://surfer.nmr.mgh.harvard.edu/fswiki/PhotoTools ).
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Bourached A, Bonkhoff AK, Schirmer MD, Regenhardt RW, Bretzner M, Hong S, Dalca AV, Giese AK, Winzeck S, Jern C, Lindgren AG, Maguire J, Wu O, Rhee J, Kimchi EY, Rost NS. Scaling behaviours of deep learning and linear algorithms for the prediction of stroke severity. Brain Commun 2024; 6:fcae007. [PMID: 38274570 PMCID: PMC10808016 DOI: 10.1093/braincomms/fcae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 09/01/2023] [Accepted: 01/09/2024] [Indexed: 01/27/2024] Open
Abstract
Deep learning has allowed for remarkable progress in many medical scenarios. Deep learning prediction models often require 105-107 examples. It is currently unknown whether deep learning can also enhance predictions of symptoms post-stroke in real-world samples of stroke patients that are often several magnitudes smaller. Such stroke outcome predictions however could be particularly instrumental in guiding acute clinical and rehabilitation care decisions. We here compared the capacities of classically used linear and novel deep learning algorithms in their prediction of stroke severity. Our analyses relied on a total of 1430 patients assembled from the MRI-Genetics Interface Exploration collaboration and a Massachusetts General Hospital-based study. The outcome of interest was National Institutes of Health Stroke Scale-based stroke severity in the acute phase after ischaemic stroke onset, which we predict by means of MRI-derived lesion location. We automatically derived lesion segmentations from diffusion-weighted clinical MRI scans, performed spatial normalization and included a principal component analysis step, retaining 95% of the variance of the original data. We then repeatedly separated a train, validation and test set to investigate the effects of sample size; we subsampled the train set to 100, 300 and 900 and trained the algorithms to predict the stroke severity score for each sample size with regularized linear regression and an eight-layered neural network. We selected hyperparameters on the validation set. We evaluated model performance based on the explained variance (R2) in the test set. While linear regression performed significantly better for a sample size of 100 patients, deep learning started to significantly outperform linear regression when trained on 900 patients. Average prediction performance improved by ∼20% when increasing the sample size 9× [maximum for 100 patients: 0.279 ± 0.005 (R2, 95% confidence interval), 900 patients: 0.337 ± 0.006]. In summary, for sample sizes of 900 patients, deep learning showed a higher prediction performance than typically employed linear methods. These findings suggest the existence of non-linear relationships between lesion location and stroke severity that can be utilized for an improved prediction performance for larger sample sizes.
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Affiliation(s)
- Anthony Bourached
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- UCL Queen Square Institute of Neurology, University College London, London WC1N 3BG, UK
| | - Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- University of Lille, Inserm, CHU Lille, U1171—LilNCog (JPARC)—Lille Neurosciences & Cognition, Lille F-59000, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg 20251, Germany
| | - Stefan Winzeck
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Computing, Imperial College London, London SW7 2RH, UK
| | - Christina Jern
- Institute of Biomedicine, Department of Laboratory Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg 41390, Sweden
- Department of Clinical Genetics and Genomics Gothenburg, Region Västra Götaland, Sahlgrenska University Hospital, Gothenburg 41345, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund 22185, Sweden
| | - Jane Maguire
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund 22185, Sweden
- University of Technology Sydney, Ultimo, NSW 2007, Australia
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - John Rhee
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02139, USA
| | - Eyal Y Kimchi
- Department of Neurology, Feinberg School of Medicine, Northwestern University, Evaston, IL 60201, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
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5
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Wang AQ, Yu EM, Dalca AV, Sabuncu MR. A robust and interpretable deep learning framework for multi-modal registration via keypoints. Med Image Anal 2023; 90:102962. [PMID: 37769550 PMCID: PMC10591968 DOI: 10.1016/j.media.2023.102962] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Revised: 08/24/2023] [Accepted: 09/07/2023] [Indexed: 10/03/2023]
Abstract
We present KeyMorph, a deep learning-based image registration framework that relies on automatically detecting corresponding keypoints. State-of-the-art deep learning methods for registration often are not robust to large misalignments, are not interpretable, and do not incorporate the symmetries of the problem. In addition, most models produce only a single prediction at test-time. Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression. We use this observation to drive the end-to-end learning of keypoints tailored for the registration task, and without knowledge of ground-truth keypoints. This framework not only leads to substantially more robust registration but also yields better interpretability, since the keypoints reveal which parts of the image are driving the final alignment. Moreover, KeyMorph can be designed to be equivariant under image translations and/or symmetric with respect to the input image ordering. Finally, we show how multiple deformation fields can be computed efficiently and in closed-form at test time corresponding to different transformation variants. We demonstrate the proposed framework in solving 3D affine and spline-based registration of multi-modal brain MRI scans. In particular, we show registration accuracy that surpasses current state-of-the-art methods, especially in the context of large displacements. Our code is available at https://github.com/alanqrwang/keymorph.
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Affiliation(s)
- Alan Q Wang
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY 10044, USA; Department of Radiology, Weill Cornell Medical School, New York, NY 10065, USA.
| | - Evan M Yu
- Iterative Scopes, Cambridge, MA 02139, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab at the Massachusetts Institute of Technology, Cambridge, MA 02139, USA; A.A. Martinos Center for Biomedical Imaging at the Massachusetts General Hospital, Charlestown, MA 02129, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University and Cornell Tech, New York, NY 10044, USA; Department of Radiology, Weill Cornell Medical School, New York, NY 10065, USA
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6
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Li J, Tuckute G, Fedorenko E, Edlow BL, Fischl B, Dalca AV. Joint cortical registration of geometry and function using semi-supervised learning. ArXiv 2023:arXiv:2303.01592v4. [PMID: 37744470 PMCID: PMC10516111] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces. Existing iterative and learning-based approaches focus on accurate registration of folding patterns of the cerebral cortex, and assume that geometry predicts function and thus functional areas will also be well aligned. However, structure/functional variability of anatomically corresponding areas across subjects has been widely reported. In this work, we introduce a learning-based cortical registration framework, JOSA, which jointly aligns folding patterns and functional maps while simultaneously learning an optimal atlas. We demonstrate that JOSA can substantially improve registration performance in both anatomical and functional domains over existing methods. By employing a semi-supervised training strategy, the proposed framework obviates the need for functional data during inference, enabling its use in broad neuroscientific domains where functional data may not be observed. The source code of JOSA will be released to the public at https://voxelmorph.net.
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Affiliation(s)
- Jian Li
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
| | - Greta Tuckute
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
| | - Evelina Fedorenko
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology
- McGovern Institute for Brain Research, Massachusetts Institute of Technology
- Program in Speech Hearing Bioscience and Technology, Harvard University
| | - Brian L Edlow
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School
| | - Bruce Fischl
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
- Harvard-MIT Program in Health Sciences and Technology
| | - Adrian V Dalca
- A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
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7
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Costantini I, Morgan L, Yang J, Balbastre Y, Varadarajan D, Pesce L, Scardigli M, Mazzamuto G, Gavryusev V, Castelli FM, Roffilli M, Silvestri L, Laffey J, Raia S, Varghese M, Wicinski B, Chang S, Chen IA, Wang H, Cordero D, Vera M, Nolan J, Nestor K, Mora J, Iglesias JE, Garcia Pallares E, Evancic K, Augustinack JC, Fogarty M, Dalca AV, Frosch MP, Magnain C, Frost R, van der Kouwe A, Chen SC, Boas DA, Pavone FS, Fischl B, Hof PR. A cellular resolution atlas of Broca's area. Sci Adv 2023; 9:eadg3844. [PMID: 37824623 PMCID: PMC10569704 DOI: 10.1126/sciadv.adg3844] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/03/2023] [Indexed: 10/14/2023]
Abstract
Brain cells are arranged in laminar, nuclear, or columnar structures, spanning a range of scales. Here, we construct a reliable cell census in the frontal lobe of human cerebral cortex at micrometer resolution in a magnetic resonance imaging (MRI)-referenced system using innovative imaging and analysis methodologies. MRI establishes a macroscopic reference coordinate system of laminar and cytoarchitectural boundaries. Cell counting is obtained with a digital stereological approach on the 3D reconstruction at cellular resolution from a custom-made inverted confocal light-sheet fluorescence microscope (LSFM). Mesoscale optical coherence tomography enables the registration of the distorted histological cell typing obtained with LSFM to the MRI-based atlas coordinate system. The outcome is an integrated high-resolution cellular census of Broca's area in a human postmortem specimen, within a whole-brain reference space atlas.
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Affiliation(s)
- Irene Costantini
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Biology, University of Florence, Florence, Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
| | - Leah Morgan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jiarui Yang
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Yael Balbastre
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Divya Varadarajan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Luca Pesce
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
| | - Marina Scardigli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Division of Physiology, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy
| | - Giacomo Mazzamuto
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Vladislav Gavryusev
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Filippo Maria Castelli
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
- Bioretics srl, Cesena, Italy
| | | | - Ludovico Silvestri
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Jessie Laffey
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sophia Raia
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Merina Varghese
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bridget Wicinski
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Shuaibin Chang
- Department of Electrical and Computer Engineering, Boston University, Boston, MA, USA
| | | | - Hui Wang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Devani Cordero
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Matthew Vera
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Jackson Nolan
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kimberly Nestor
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jocelyn Mora
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Juan Eugenio Iglesias
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Medical Physics and Biomedical Engineering, University College London, London, UK
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Erendira Garcia Pallares
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
| | - Kathryn Evancic
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Jean C. Augustinack
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Morgan Fogarty
- Imaging Science Program, Washington University McKelvey School of Engineering, St. Louis, MO, USA
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Adrian V. Dalca
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Matthew P. Frosch
- C.S. Kubik Laboratory for Neuropathology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Caroline Magnain
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Robert Frost
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Andre van der Kouwe
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Department of Human Biology, University of Cape Town, Cape Town, South Africa
| | - Shih-Chi Chen
- Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong
| | - David A. Boas
- Department of Biomedical Engineering, Boston University, Boston, MA, USA
| | - Francesco Saverio Pavone
- European Laboratory for Non-Linear Spectroscopy (LENS), University of Florence, Sesto Fiorentino (FI), Italy
- National Institute of Optics (INO), National Research Council (CNR), Sesto Fiorentino, Italy
- Department of Physics and Astronomy, University of Florence, Sesto Fiorentino (FI), Italy
| | - Bruce Fischl
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
- HST, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Patrick R. Hof
- Nash Family Department of Neuroscience and Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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8
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Kazi A, Mora J, Fischl B, Dalca AV, Aganj I. Multi-Head Graph Convolutional Network for Structural Connectome Classification. ArXiv 2023:arXiv:2305.02199v2. [PMID: 37205262 PMCID: PMC10187363] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain connectivity input graph and processes the data separately through a parallel GCN mechanism with multiple heads. The proposed network is a simple design that employs different heads involving graph convolutions focused on edges and nodes, capturing representations from the input data thoroughly. To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification. This quantifies the degree to which the connectome varies depending on the sex, which is important for improving our understanding of health and disease in both sexes. We show experiments on two publicly available datasets: PREVENT-AD (347 subjects) and OASIS3 (771 subjects). The proposed model demonstrates the highest performance compared to the existing machine-learning algorithms we tested, including classical methods and (graph and non-graph) deep learning. We provide a detailed analysis of each component of our model.
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9
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Young SI, Dalca AV, Ferrante E, Golland P, Metzler CA, Fischl B, Iglesias JE. Supervision by Denoising. IEEE Trans Pattern Anal Mach Intell 2023; PP:1-12. [PMID: 37505997 DOI: 10.1109/tpami.2023.3299789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/30/2023]
Abstract
Learning-based image reconstruction models, such as those based on the U-Net, require a large set of labeled images if good generalization is to be guaranteed. In some imaging domains, however, labeled data with pixel- or voxel-level label accuracy are scarce due to the cost of acquiring them. This problem is exacerbated further in domains like medical imaging, where there is no single ground truth label, resulting in large amounts of repeat variability in the labels. Therefore, training reconstruction networks to generalize better by learning from both labeled and unlabeled examples (called semi-supervised learning) is problem of practical and theoretical interest. However, traditional semi-supervised learning methods for image reconstruction often necessitate handcrafting a differentiable regularizer specific to some given imaging problem, which can be extremely time-consuming. In this work, we propose "supervision by denoising" (SUD), a framework to supervise reconstruction models using their own denoised output as labels. SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision. As example applications, we apply SUD to two problems from biomedical imaging-anatomical brain reconstruction (3D) and cortical parcellation (2D)-to demonstrate a significant improvement in reconstruction over supervised-only and ensembling baselines. Our code available at https://github.com/seannz/sud.
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10
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Hoffmann M, Singh NM, Dalca AV, Fischl B, Frost R. Can we predict motion artifacts in clinical MRI before the scan completes? Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib 2023; 2023:1372. [PMID: 37692094 PMCID: PMC10490829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Subject motion can cause artifacts in clinical MRI, frequently necessitating repeat scans. We propose to alleviate this inefficiency by predicting artifact scores from partial multi-shot multi-slice acquisitions, which may guide the operator in aborting corrupted scans early.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Nalini M Singh
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA 02139, USA
- Harvard-MIT Division of Health Sciences and Technology, MIT, Cambridge, MA 02139, USA
| | - Robert Frost
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
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11
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Ma T, Wang AQ, Dalca AV, Sabuncu MR. Hyper-convolutions via implicit kernels for medical image analysis. Med Image Anal 2023; 86:102796. [PMID: 36948069 DOI: 10.1016/j.media.2023.102796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 08/08/2022] [Accepted: 03/06/2023] [Indexed: 03/18/2023]
Abstract
The convolutional neural network (CNN) is one of the most commonly used architectures for computer vision tasks. The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels. A standard CNN's capacity, and thus its performance, is directly related to the number of learnable kernel weights, which is determined by the number of channels and the kernel size (support). In this paper, we present the hyper-convolution, a novel building block that implicitly encodes the convolutional kernel using spatial coordinates. Unlike a regular convolutional kernel, whose weights are independently learned, hyper-convolution kernel weights are correlated through an encoder that maps spatial coordinates to their corresponding values. Hyper-convolutions decouple kernel size from the total number of learnable parameters, enabling a more flexible architecture design. We demonstrate in our experiments that replacing regular convolutions with hyper-convolutions can improve performance with less parameters, and increase robustness against noise. We provide our code here: https://github.com/tym002/Hyper-Convolution.
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Affiliation(s)
- Tianyu Ma
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA; Cornell Tech, NYC, NY, USA; Department of Radiology, Weill Cornell Medical School, NYC, NY, USA
| | - Alan Q Wang
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA; Cornell Tech, NYC, NY, USA; Department of Radiology, Weill Cornell Medical School, NYC, NY, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Mert R Sabuncu
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY, USA; Cornell Tech, NYC, NY, USA; Department of Radiology, Weill Cornell Medical School, NYC, NY, USA.
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12
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Hering A, Hansen L, Mok TCW, Chung ACS, Siebert H, Hager S, Lange A, Kuckertz S, Heldmann S, Shao W, Vesal S, Rusu M, Sonn G, Estienne T, Vakalopoulou M, Han L, Huang Y, Yap PT, Brudfors M, Balbastre Y, Joutard S, Modat M, Lifshitz G, Raviv D, Lv J, Li Q, Jaouen V, Visvikis D, Fourcade C, Rubeaux M, Pan W, Xu Z, Jian B, De Benetti F, Wodzinski M, Gunnarsson N, Sjolund J, Grzech D, Qiu H, Li Z, Thorley A, Duan J, Grosbrohmer C, Hoopes A, Reinertsen I, Xiao Y, Landman B, Huo Y, Murphy K, Lessmann N, van Ginneken B, Dalca AV, Heinrich MP. Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning. IEEE Trans Med Imaging 2023; 42:697-712. [PMID: 36264729 DOI: 10.1109/tmi.2022.3213983] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.
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13
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Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese A, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez‐Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah C, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Wu O, Rost NS. The relevance of rich club regions for functional outcome post-stroke is enhanced in women. Hum Brain Mapp 2023; 44:1579-1592. [PMID: 36440953 PMCID: PMC9921242 DOI: 10.1002/hbm.26159] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2022] [Revised: 10/24/2022] [Accepted: 11/11/2022] [Indexed: 11/30/2022] Open
Abstract
This study aimed to investigate the influence of stroke lesions in predefined highly interconnected (rich-club) brain regions on functional outcome post-stroke, determine their spatial specificity and explore the effects of biological sex on their relevance. We analyzed MRI data recorded at index stroke and ~3-months modified Rankin Scale (mRS) data from patients with acute ischemic stroke enrolled in the multisite MRI-GENIE study. Spatially normalized structural stroke lesions were parcellated into 108 atlas-defined bilateral (sub)cortical brain regions. Unfavorable outcome (mRS > 2) was modeled in a Bayesian logistic regression framework. Effects of individual brain regions were captured as two compound effects for (i) six bilateral rich club and (ii) all further non-rich club regions. In spatial specificity analyses, we randomized the split into "rich club" and "non-rich club" regions and compared the effect of the actual rich club regions to the distribution of effects from 1000 combinations of six random regions. In sex-specific analyses, we introduced an additional hierarchical level in our model structure to compare male and female-specific rich club effects. A total of 822 patients (age: 64.7[15.0], 39% women) were analyzed. Rich club regions had substantial relevance in explaining unfavorable functional outcome (mean of posterior distribution: 0.08, area under the curve: 0.8). In particular, the rich club-combination had a higher relevance than 98.4% of random constellations. Rich club regions were substantially more important in explaining long-term outcome in women than in men. All in all, lesions in rich club regions were associated with increased odds of unfavorable outcome. These effects were spatially specific and more pronounced in women.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Martin Bretzner
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Univ. Lille, Inserm, CHU Lille, U1171 – LilNCog (JPARC) – Lille Neurosciences & CognitionLilleFrance
| | - Sungmin Hong
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence LabMassachusetts Institute of TechnologyBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Anne‐Katrin Giese
- Department of NeurologyUniversity Medical Center Hamburg‐EppendorfHamburgGermany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Brandon L. Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Elissa C. McIntosh
- Department of PsychiatryJohns Hopkins School of MedicineBaltimoreMarylandUSA
| | - John Attia
- Hunter Medical Research InstituteNewcastleNew South WalesAustralia
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
| | - John W. Cole
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Amanda Donatti
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Christoph J. Griessenauer
- Department of NeurosurgeryGeisingerDanvillePennsylvaniaUSA
- Research Institute of NeurointerventionParacelsus Medical UniversitySalzburgAustria
| | - Laura Heitsch
- Department of Emergency MedicineWashington University School of MedicineSt LouisMissouriUSA
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Lukas Holmegaard
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Katarina Jood
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Jordi Jimenez‐Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Steven J. Kittner
- Department of NeurologyUniversity of Maryland School of Medicine and Veterans Affairs Maryland Health Care SystemBaltimoreMarylandUSA
| | - Robin Lemmens
- Department of NeurosciencesKU Leuven – University of Leuven, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND)LeuvenBelgium
- Department of Neurology, VIB, Vesalius Research CenterLaboratory of Neurobiology, University Hospitals LeuvenLeuvenBelgium
| | - Christopher R. Levi
- School of Medicine and Public HealthUniversity of NewcastleNewcastleNew South WalesAustralia
- Department of NeurologyJohn Hunter HospitalNewcastleNew South WalesAustralia
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for PharmacogenomicsUniversity of FloridaGainesvilleFloridaUSA
| | | | - Chia‐Ling Phuah
- Department of NeurologyWashington University School of Medicine & Barnes‐Jewish HospitalSt LouisMissouriUSA
| | - Stefan Ropele
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Henry and Allison McCance Center for Brain HealthMassachusetts General HospitalBostonMassachusettsUSA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM‐Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques). Department of Medicine and Life Sciences (MELIS)Universitat Pompeu FabraBarcelonaSpain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of MedicineUniversity of MiamiMiamiFloridaUSA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of NeurogeriatricsMedical University GrazGrazAustria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL)EghamUK
- St Peter's and Ashford HospitalsAshfordUK
| | - Agnieszka Slowik
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
| | - Alessandro Sousa
- School of Medical SciencesUniversity of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN)CampinasSão PauloBrazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska AcademyUniversity of GothenburgGothenburgSweden
| | - Daniel Strbian
- Department of NeurologyHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Turgut Tatlisumak
- Department of Clinical NeuroscienceInstitute of Neuroscience and Physiology, Sahlgrenska Academy, University of GothenburgGothenburgSweden
- Department of NeurologySahlgrenska University HospitalGothenburgSweden
| | - Vincent Thijs
- Stroke DivisionFlorey Institute of Neuroscience and Mental HealthHeidelbergAustralia
- Department of NeurologyAustin HealthHeidelbergAustralia
| | - Achala Vagal
- Department of RadiologyUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, RadiologyLund UniversityLundSweden
- Department of Radiology, NeuroradiologySkåne University HospitalLundSweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation MedicineUniversity of Cincinnati College of MedicineCincinnatiOhioUSA
| | - Ramin Zand
- Department of NeurologyPennsylvania State UniversityHersheyPennsylvaniaUSA
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of MedicineUniversity of Maryland School of MedicineBaltimoreMarylandUSA
| | - Bradford B. Worrall
- Departments of Neurology and Public Health SciencesUniversity of VirginiaCharlottesvilleVirginiaUSA
| | - Christina Jern
- Department of NeurologyJagiellonian University Medical CollegeKrakowPoland
- Department of Clinical Genetics and GenomicsSahlgrenska University HospitalGothenburgSweden
| | - Arne G. Lindgren
- Department of NeurologySkåne University HospitalLundSweden
- Department of Clinical Sciences Lund, NeurologyLund UniversityLundSweden
| | | | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research CenterMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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Billot B, Greve DN, Puonti O, Thielscher A, Van Leemput K, Fischl B, Dalca AV, Iglesias JE. SynthSeg: Segmentation of brain MRI scans of any contrast and resolution without retraining. Med Image Anal 2023; 86:102789. [PMID: 36857946 PMCID: PMC10154424 DOI: 10.1016/j.media.2023.102789] [Citation(s) in RCA: 31] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2022] [Revised: 01/20/2023] [Accepted: 02/22/2023] [Indexed: 03/03/2023]
Abstract
Despite advances in data augmentation and transfer learning, convolutional neural networks (CNNs) difficultly generalise to unseen domains. When segmenting brain scans, CNNs are highly sensitive to changes in resolution and contrast: even within the same MRI modality, performance can decrease across datasets. Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution. SynthSeg is trained with synthetic data sampled from a generative model conditioned on segmentations. Crucially, we adopt a domain randomisation strategy where we fully randomise the contrast and resolution of the synthetic training data. Consequently, SynthSeg can segment real scans from a wide range of target domains without retraining or fine-tuning, which enables straightforward analysis of huge amounts of heterogeneous clinical data. Because SynthSeg only requires segmentations to be trained (no images), it can learn from labels obtained by automated methods on diverse populations (e.g., ageing and diseased), thus achieving robustness to a wide range of morphological variability. We demonstrate SynthSeg on 5,000 scans of six modalities (including CT) and ten resolutions, where it exhibits unparallelled generalisation compared with supervised CNNs, state-of-the-art domain adaptation, and Bayesian segmentation. Finally, we demonstrate the generalisability of SynthSeg by applying it to cardiac MRI and CT scans.
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Affiliation(s)
- Benjamin Billot
- Centre for Medical Image Computing, University College London, UK.
| | - Douglas N Greve
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA
| | - Oula Puonti
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Denmark
| | - Axel Thielscher
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Denmark; Department of Health Technology, Technical University of, Denmark
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Department of Health Technology, Technical University of, Denmark
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA; Program in Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, UK; Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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15
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Abstract
The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams. Despite their abundance, popular classical skull-stripping methods are usually tailored to images with specific acquisition properties, namely near-isotropic resolution and T1-weighted (T1w) MRI contrast, which are prevalent in research settings. As a result, existing tools tend to adapt poorly to other image types, such as stacks of thick slices acquired with fast spin-echo (FSE) MRI that are common in the clinic. While learning-based approaches for brain extraction have gained traction in recent years, these methods face a similar burden, as they are only effective for image types seen during the training procedure. To achieve robust skull-stripping across a landscape of imaging protocols, we introduce SynthStrip, a rapid, learning-based brain-extraction tool. By leveraging anatomical segmentations to generate an entirely synthetic training dataset with anatomies, intensity distributions, and artifacts that far exceed the realistic range of medical images, SynthStrip learns to successfully generalize to a variety of real acquired brain images, removing the need for training data with target contrasts. We demonstrate the efficacy of SynthStrip for a diverse set of image acquisitions and resolutions across subject populations, ranging from newborn to adult. We show substantial improvements in accuracy over popular skull-stripping baselines - all with a single trained model. Our method and labeled evaluation data are available at https://w3id.org/synthstrip.
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Affiliation(s)
- Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA
| | - Jocelyn S Mora
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, 32 Vassar St, Cambridge, MA, USA; Harvard-MIT Division of Health Sciences and Technology, 77 Massachusetts Ave, Cambridge, MA, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, 149 13(th) St, Charlestown, MA, USA; Department of Radiology, Harvard Medical School, 25 Shattuck St, Boston, MA, USA.
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16
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Bonkhoff AK, Ullberg T, Bretzner M, Hong S, Schirmer MD, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Wu O, Frid P, Rost NS, Wasselius J. Deep profiling of multiple ischemic lesions in a large, multi-center cohort: Frequency, spatial distribution, and associations to clinical characteristics. Front Neurosci 2022; 16:994458. [PMID: 36090258 PMCID: PMC9453031 DOI: 10.3389/fnins.2022.994458] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 08/02/2022] [Indexed: 11/17/2022] Open
Abstract
Background purpose A substantial number of patients with acute ischemic stroke (AIS) experience multiple acute lesions (MAL). We here aimed to scrutinize MAL in a large radiologically deep-phenotyped cohort. Materials and methods Analyses relied upon imaging and clinical data from the international MRI-GENIE study. Imaging data comprised both Fluid-attenuated inversion recovery (FLAIR) for white matter hyperintensity (WMH) burden estimation and diffusion-weighted imaging (DWI) sequences for the assessment of acute stroke lesions. The initial step featured the systematic evaluation of occurrences of MAL within one and several vascular supply territories. Associations between MAL and important imaging and clinical characteristics were subsequently determined. The interaction effect between single and multiple lesion status and lesion volume was estimated by means of Bayesian hierarchical regression modeling for both stroke severity and functional outcome. Results We analyzed 2,466 patients (age = 63.4 ± 14.8, 39% women), 49.7% of which presented with a single lesion. Another 37.4% experienced MAL in a single vascular territory, while 12.9% featured lesions in multiple vascular territories. Within most territories, MAL occurred as frequently as single lesions (ratio ∼1:1). Only the brainstem region comprised fewer patients with MAL (ratio 1:4). Patients with MAL presented with a significantly higher lesion volume and acute NIHSS (7.7 vs. 1.7 ml and 4 vs. 3, p FDR < 0.001). In contrast, patients with a single lesion were characterized by a significantly higher WMH burden (6.1 vs. 5.3 ml, p FDR = 0.048). Functional outcome did not differ significantly between patients with single versus multiple lesions. Bayesian analyses suggested that the association between lesion volume and stroke severity between single and multiple lesions was the same in case of anterior circulation stroke. In case of posterior circulation stroke, lesion volume was linked to a higher NIHSS only among those with MAL. Conclusion Multiple lesions, especially those within one vascular territory, occurred more frequently than previously reported. Overall, multiple lesions were distinctly linked to a higher acute stroke severity, a higher total DWI lesion volume and a lower WMH lesion volume. In posterior circulation stroke, lesion volume was linked to a higher stroke severity in multiple lesions only.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Teresa Ullberg
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology and Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- U1171 – LilNCog (JPARC) – Lille Neurosciences Cognition and University of Lille, Inserm, CHU Lille, Lille, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Brandon L. Hancock
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Steven J. T. Mocking
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Elissa C. McIntosh
- Department of Psychiatry, Johns Hopkins School of Medicine, Baltimore, MD, United States
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - John W. Cole
- Department of Neurology, University of Maryland, School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Amanda Donatti
- School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas (UNICAMP), Campinas, Brazil
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, United States
- Department of Neurosurgery, Christian Doppler Clinic, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Steven J. Kittner
- Department of Neurology, University of Maryland, School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience, Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium
- Laboratory of Neurobiology, Department of Neurology, Vesalius Research Center (VIB), University Hospitals Leuven, Leuven, Belgium
| | - Christopher R. Levi
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
- Department of Pharmacotherapy, Translational Research, Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States
| | | | - James F. Meschia
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Chia-Ling Phuah
- Department of Neurology, Barnes-Jewish Hospital, Washington University School of Medicine, St. Louis, MO, United States
| | - Stefan Ropele
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Department of Neurology, Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Pompeu Fabra, Barcelona, Spain
- Department of Medicine and Life Sciences (MELIS), Universitat Pompeu Fabra, Barcelona, Spain
| | - Tatjana Rundek
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Ralph L. Sacco
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Reinhold Schmidt
- Henry and Allison McCance Center for Brain Health, Massachusetts General Hospital, Boston, MA, United States
| | - Pankaj Sharma
- Institute of Cardiovascular Research, St Peter’s, Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - Alessandro Sousa
- School of Medical Sciences, The Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), University of Campinas (UNICAMP), Campinas, Brazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Division of Stroke, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Daniel Woo
- Department of Neurology, Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ramin Zand
- Department of Neurology, Pennsylvania State University, Hershey, PA, United States
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Bradford B. Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, United States
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G. Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology, Faculty of Health, Sydney, NSW, Australia
| | - Ona Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology and Neuroradiology, Skåne University Hospital, Lund, Sweden
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17
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Pace DF, Dalca AV, Brosch T, Geva T, Powell AJ, Weese J, Moghari MH, Golland P. Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease. Med Image Anal 2022; 80:102469. [PMID: 35640385 PMCID: PMC9617683 DOI: 10.1016/j.media.2022.102469] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 04/26/2022] [Accepted: 04/29/2022] [Indexed: 02/08/2023]
Abstract
Training deep learning models that segment an image in one step typically requires a large collection of manually annotated images that captures the anatomical variability in a cohort. This poses challenges when anatomical variability is extreme but training data is limited, as when segmenting cardiac structures in patients with congenital heart disease (CHD). In this paper, we propose an iterative segmentation model and show that it can be accurately learned from a small dataset. Implemented as a recurrent neural network, the model evolves a segmentation over multiple steps, from a single user click until reaching an automatically determined stopping point. We develop a novel loss function that evaluates the entire sequence of output segmentations, and use it to learn model parameters. Segmentations evolve predictably according to growth dynamics encapsulated by training data, which consists of images, partially completed segmentations, and the recommended next step. The user can easily refine the final segmentation by examining those that are earlier or later in the output sequence. Using a dataset of 3D cardiac MR scans from patients with a wide range of CHD types, we show that our iterative model offers better generalization to patients with the most severe heart malformations.
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Affiliation(s)
- Danielle F Pace
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA; A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Tom Brosch
- Philips Research Laboratories, Hamburg, Germany
| | - Tal Geva
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | | | - Mehdi H Moghari
- Department of Cardiology, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
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18
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Frid P, Xu H, Mitchell BD, Drake M, Wasselius J, Gaynor B, Ryan K, Giese AK, Schirmer M, Donahue KL, Irie R, Bouts MJRJ, McIntosh EC, Mocking SJT, Dalca AV, Giralt-Steinhauer E, Holmegaard L, Jood K, Roquer J, Cole JW, McArdle PF, Broderick JP, Jimenez-Conde J, Jern C, Kissela BM, Kleindorfer DO, Lemmens R, Meschia JF, Rosand J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Worrall BB, Kittner SJ, Petersson J, Golland P, Wu O, Rost NS, Lindgren A. Migraine-Associated Common Genetic Variants Confer Greater Risk of Posterior vs. Anterior Circulation Ischemic Stroke☆. J Stroke Cerebrovasc Dis 2022; 31:106546. [PMID: 35576861 PMCID: PMC10601407 DOI: 10.1016/j.jstrokecerebrovasdis.2022.106546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2022] [Accepted: 05/01/2022] [Indexed: 11/20/2022] Open
Abstract
OBJECTIVE To examine potential genetic relationships between migraine and the two distinct phenotypes posterior circulation ischemic stroke (PCiS) and anterior circulation ischemic stroke (ACiS), we generated migraine polygenic risk scores (PRSs) and compared these between PCiS and ACiS, and separately vs. non-stroke control subjects. METHODS Acute ischemic stroke cases were classified as PCiS or ACiS based on lesion location on diffusion-weighted MRI. Exclusion criteria were lesions in both vascular territories or uncertain territory; supratentorial PCiS with ipsilateral fetal posterior cerebral artery; and cases with atrial fibrillation. We generated migraine PRS for three migraine phenotypes (any migraine; migraine without aura; migraine with aura) using publicly available GWAS data and compared mean PRSs separately for PCiS and ACiS vs. non-stroke control subjects, and between each stroke phenotype. RESULTS Our primary analyses included 464 PCiS and 1079 ACiS patients with genetic European ancestry. Compared to non-stroke control subjects (n=15396), PRSs of any migraine were associated with increased risk of PCiS (p=0.01-0.03) and decreased risk of ACiS (p=0.010-0.039). Migraine without aura PRSs were significantly associated with PCiS (p=0.008-0.028), but not with ACiS. When comparing PCiS vs. ACiS directly, migraine PRSs were higher in PCiS vs. ACiS for any migraine (p=0.001-0.010) and migraine without aura (p=0.032-0.048). Migraine with aura PRS did not show a differential association in our analyses. CONCLUSIONS Our results suggest a stronger genetic overlap between unspecified migraine and migraine without aura with PCiS compared to ACiS. Possible shared mechanisms include dysregulation of cerebral vessel endothelial function.
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Affiliation(s)
- P Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; Section of Neurology, Skåne University Hospital, Malmö, Sweden.
| | - H Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA.
| | - B D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA; Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, USA
| | - M Drake
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - J Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - B Gaynor
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - K Ryan
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - A K Giese
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - M Schirmer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - K L Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R Irie
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - M J R J Bouts
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - E C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - S J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - A V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
| | - E Giralt-Steinhauer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain
| | - L Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - K Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - J Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain
| | - J W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - P F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - J P Broderick
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - J Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain
| | - C Jern
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden; Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - B M Kissela
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - D O Kleindorfer
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - R Lemmens
- Department of Neurosciences, Experimental Neurology, VIB Center for Brain & Disease Research, Department of Neurology, University Hospitals Leuven, KU Leuven - University of Leuven, Leuven, Belgium
| | - J F Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - J Rosand
- Henry and Allison McCance Center for Brain Health Massachusetts General Hospital, Boston, USA
| | - T Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL, USA
| | - R L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL, USA
| | - R Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Austria
| | - P Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
| | - A Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - V Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, and Department of Neurology, Austin Health, Heidelberg, Australia
| | - D Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - B B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - S J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - J Petersson
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - P Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
| | - O Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - N S Rost
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; Section of Neurology, Skåne University Hospital, Lund, Sweden
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19
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Young SI, Balbastre Y, Dalca AV, Wells WM, Iglesias JE, Fischl B. SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration. Biomed Image Regist (2022) 2022; 13386:103-115. [PMID: 36383500 PMCID: PMC9645132 DOI: 10.1007/978-3-031-11203-4_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks. These approaches utilize a loss function that penalizes the intensity differences between the fixed and moving images, along with a suitable regularizer on the deformation. However, since images typically have large untextured regions, merely maximizing similarity between the two images is not sufficient to recover the true deformation. This problem is exacerbated by texture in other regions, which introduces severe non-convexity into the landscape of the training objective and ultimately leads to overfitting. In this paper, we argue that the relative failure of supervised registration approaches can in part be blamed on the use of regular U-Nets, which are jointly tasked with feature extraction, feature matching and deformation estimation. Here, we introduce a simple but crucial modification to the U-Net that disentangles feature extraction and matching from deformation prediction, allowing the U-Net to warp the features, across levels, as the deformation field is evolved. With this modification, direct supervision using target warps begins to outperform self-supervision approaches that require segmentations, presenting new directions for registration when images do not have segmentations. We hope that our findings in this preliminary workshop paper will re-ignite research interest in supervised image registration techniques. Our code is publicly available from http://github.com/balbasty/superwarp.
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Affiliation(s)
- Sean I Young
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
| | - Yaël Balbastre
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
| | - William M Wells
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging and Massachusetts Institute of Technology
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20
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Hoopes A, Iglesias JE, Fischl B, Greve D, Dalca AV. TopoFit: Rapid Reconstruction of Topologically-Correct Cortical Surfaces. Proc Mach Learn Res 2022; 172:508-520. [PMID: 37220495 PMCID: PMC10201930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Mesh-based reconstruction of the cerebral cortex is a fundamental component in brain image analysis. Classical, iterative pipelines for cortical modeling are robust but often time-consuming, mostly due to expensive procedures that involve topology correction and spherical mapping. Recent attempts to address reconstruction with machine learning methods have accelerated some components in these pipelines, but these methods still require slow processing steps to enforce topological constraints that comply with known anatomical structure. In this work, we introduce a novel learning-based strategy, TopoFit, which rapidly fits a topologically-correct surface to the white-matter tissue boundary. We design a joint network, employing image and graph convolutions and an efficient symmetric distance loss, to learn to predict accurate deformations that map a template mesh to subject-specific anatomy. This technique encompasses the work of current mesh correction, fine-tuning, and inflation processes and, as a result, offers a 150× faster solution to cortical surface reconstruction compared to traditional approaches. We demonstrate that TopoFit is 1.8× more accurate than the current state-of-the-art deep-learning strategy, and it is robust to common failure modes, such as white-matter tissue hypointensities.
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Affiliation(s)
- Andrew Hoopes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Centre for Medical Image Computing, University College London
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
- Harvard-MIT Division of Health, Sciences, and Technology
| | - Douglas Greve
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
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21
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Singh NM, Iglesias JE, Adalsteinsson E, Dalca AV, Golland P. Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis. J Mach Learn Biomed Imaging 2022; 2022:018. [PMID: 36349348 PMCID: PMC9639401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
We propose neural network layers that explicitly combine frequency and image feature representations and show that they can be used as a versatile building block for reconstruction from frequency space data. Our work is motivated by the challenges arising in MRI acquisition where the signal is a corrupted Fourier transform of the desired image. The proposed joint learning schemes enable both correction of artifacts native to the frequency space and manipulation of image space representations to reconstruct coherent image structures at every layer of the network. This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces. We demonstrate the advantages of joint convolutional learning for a variety of tasks, including motion correction, denoising, reconstruction from undersampled acquisitions, and combined undersampling and motion correction on simulated and real world multicoil MRI data. The joint models produce consistently high quality output images across all tasks and datasets. When integrated into a state of the art unrolled optimization network with physics-inspired data consistency constraints for undersampled reconstruction, the proposed architectures significantly improve the optimization landscape, which yields an order of magnitude reduction of training time. This result suggests that joint representations are particularly well suited for MRI signals in deep learning networks. Our code and pretrained models are publicly available at https://github.com/nalinimsingh/interlacer.
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Affiliation(s)
- Nalini M Singh
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Dept. of Health Sciences & Technology, MIT, Cambridge, MA, USA
| | - Juan Eugenio Iglesias
- A. A. Martinos Center, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Cambridge, MA, USA
- Centre for Medical Image Computing, UCL, London, UK
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Elfar Adalsteinsson
- Research Laboratory of Electronics, MIT, Cambridge, MA, USA
- Dept. of Electrical Engineering & Computer Science, MIT, Cambridge, MA, USA
| | - Adrian V Dalca
- A. A. Martinos Center, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Cambridge, MA, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA
- Dept. of Electrical Engineering & Computer Science, MIT, Cambridge, MA, USA
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22
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Hoffmann M, Billot B, Greve DN, Iglesias JE, Fischl B, Dalca AV. SynthMorph: Learning Contrast-Invariant Registration Without Acquired Images. IEEE Trans Med Imaging 2022; 41:543-558. [PMID: 34587005 PMCID: PMC8891043 DOI: 10.1109/tmi.2021.3116879] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to contrast introduced by magnetic resonance imaging (MRI). While classical registration methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning-based techniques are fast at test time but limited to registering images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency on training data by leveraging a generative strategy for diverse synthetic label maps and images that exposes networks to a wide range of variability, forcing them to learn more invariant features. This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts. We present extensive experiments with a focus on 3D neuroimaging, showing that this strategy enables robust and accurate registration of arbitrary MRI contrasts even if the target contrast is not seen by the networks during training. We demonstrate registration accuracy surpassing the state of the art both within and across contrasts, using a single model. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images. Our code is available at doic https://w3id.org/synthmorph.
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23
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Hoopes A, Hoffmann M, Greve DN, Fischl B, Guttag J, Dalca AV. Learning the Effect of Registration Hyperparameters with HyperMorph. J Mach Learn Biomed Imaging 2022; 1:003. [PMID: 36147449 PMCID: PMC9491317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, image contrast, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at http://hypermorph.voxelmorph.net.
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Affiliation(s)
- Andrew Hoopes
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
| | - Malte Hoffmann
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Douglas N Greve
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
| | - John Guttag
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital
- Department of Radiology, Harvard Medical School
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology
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24
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Mu E, Jabbour S, Dalca AV, Guttag J, Wiens J, Sjoding MW. Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration. PLoS One 2022; 17:e0263922. [PMID: 35167608 PMCID: PMC8846502 DOI: 10.1371/journal.pone.0263922] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 01/29/2022] [Indexed: 12/02/2022] Open
Abstract
IMPORTANCE When hospitals are at capacity, accurate deterioration indices could help identify low-risk patients as potential candidates for home care programs and alleviate hospital strain. To date, many existing deterioration indices are based entirely on structured data from the electronic health record (EHR) and ignore potentially useful information from other sources. OBJECTIVE To improve the accuracy of existing deterioration indices by incorporating unstructured imaging data from chest radiographs. DESIGN, SETTING, AND PARTICIPANTS Machine learning models were trained to predict deterioration of patients hospitalized with acute dyspnea using existing deterioration index scores and chest radiographs. Models were trained on hospitalized patients without coronavirus disease 2019 (COVID-19) and then subsequently tested on patients with COVID-19 between January 2020 and December 2020 at a single tertiary care center who had at least one radiograph taken within 48 hours of hospital admission. MAIN OUTCOMES AND MEASURES Patient deterioration was defined as the need for invasive or non-invasive mechanical ventilation, heated high flow nasal cannula, IV vasopressor administration or in-hospital mortality at any time following admission. The EPIC deterioration index was augmented with unstructured data from chest radiographs to predict risk of deterioration. We compared discriminative performance of the models with and without incorporating chest radiographs using area under the receiver operating curve (AUROC), focusing on comparing the fraction and total patients identified as low risk at different negative predictive values (NPV). RESULTS Data from 6278 hospitalizations were analyzed, including 5562 hospitalizations without COVID-19 (training cohort) and 716 with COVID-19 (216 in validation, 500 in held-out test cohort). At a NPV of 0.95, the best-performing image-augmented deterioration index identified 49 more (9.8%) individuals as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. At a NPV of 0.9, the EPIC image-augmented deterioration index identified 26 more individuals (5.2%) as low-risk compared to the deterioration index based on clinical data alone in the first 48 hours of admission. CONCLUSION AND RELEVANCE Augmenting existing deterioration indices with chest radiographs results in better identification of low-risk patients. The model augmentation strategy could be used in the future to incorporate other forms of unstructured data into existing disease models.
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Affiliation(s)
- Emily Mu
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Sarah Jabbour
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI, United States of America
| | - Adrian V. Dalca
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
- Martinos Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - John Guttag
- Department of Computer Science and Electrical Engineering, Massachusetts Institute of Technology, Cambridge, MA, United States of America
| | - Jenna Wiens
- Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI, United States of America
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States of America
| | - Michael W. Sjoding
- Institute for Healthcare Policy and Innovation, University of Michigan, Ann Arbor, MI, United States of America
- Department of Internal Medicine, University of Michigan Medical School, Ann Arbor, MI, United States of America
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25
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Bonkhoff AK, Bretzner M, Hong S, Schirmer MD, Cohen A, Regenhardt RW, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Benavente OR, Bevan S, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Söderholm M, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Fox MD, Bzdok D, Wu O, Rost NS. OUP accepted manuscript. Brain Commun 2022; 4:fcac020. [PMID: 35282166 PMCID: PMC8914504 DOI: 10.1093/braincomms/fcac020] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2021] [Revised: 11/30/2021] [Accepted: 01/31/2022] [Indexed: 11/29/2022] Open
Abstract
Stroke represents a considerable burden of disease for both men and women. However, a growing body of literature suggests clinically relevant sex differences in the underlying causes, presentations and outcomes of acute ischaemic stroke. In a recent study, we reported sex divergences in lesion topographies: specific to women, acute stroke severity was linked to lesions in the left-hemispheric posterior circulation. We here determined whether these sex-specific brain manifestations also affect long-term outcomes. We relied on 822 acute ischaemic patients [age: 64.7 (15.0) years, 39% women] originating from the multi-centre MRI-GENIE study to model unfavourable outcomes (modified Rankin Scale >2) based on acute neuroimaging data in a Bayesian hierarchical framework. Lesions encompassing bilateral subcortical nuclei and left-lateralized regions in proximity to the insula explained outcomes across men and women (area under the curve = 0.81). A pattern of left-hemispheric posterior circulation brain regions, combining left hippocampus, precuneus, fusiform and lingual gyrus, occipital pole and latero-occipital cortex, showed a substantially higher relevance in explaining functional outcomes in women compared to men [mean difference of Bayesian posterior distributions (men – women) = −0.295 (90% highest posterior density interval = −0.556 to −0.068)]. Once validated in prospective studies, our findings may motivate a sex-specific approach to clinical stroke management and hold the promise of enhancing outcomes on a population level.
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Affiliation(s)
- Anna K. Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Correspondence to: Anna K. Bonkhoff, J. Philip Kistler Stroke Research Center
Massachusetts General Hospital, Harvard Medical School
175 Cambridge St, Suite 300 Boston, MA 02114, USA
E-mail:
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Univ. Lille, Inserm, CHU Lille, U1171—LilNCog (JPARC)—Lille Neurosciences & Cognition, Lille F-59000, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Alexander Cohen
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J. Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brandon L. Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C. McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Oscar R. Benavente
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Bevan
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | - John W. Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, USA
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J. Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven—University of Leuven, Leuven, Belgium
- Department of Neurology, VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Leuven, Belgium
| | - Christopher R. Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | | | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d’Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research Royal Holloway, University of London (ICR2UL), London, UK
- St Peter’s and Ashford Hospital, Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Martin Söderholm
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund and Malmö, Malmo, Sweden
| | - Alessandro Sousa
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, SP, Brazil
| | - Tara M. Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, USA
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bradford B. Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G. Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, Australia
| | - Michael D. Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, Canada
- Mila—Quebec Artificial Intelligence Institute, Montreal, Canada
| | - Ona Wu
- Department of Neurology, Boston Children’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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26
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Li J, Curley WH, Guerin B, Dougherty DD, Dalca AV, Fischl B, Horn A, Edlow BL. Mapping the subcortical connectivity of the human default mode network. Neuroimage 2021; 245:118758. [PMID: 34838949 PMCID: PMC8945548 DOI: 10.1016/j.neuroimage.2021.118758] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 10/29/2021] [Accepted: 11/23/2021] [Indexed: 01/17/2023] Open
Abstract
The default mode network (DMN) mediates self-awareness and introspection, core components of human consciousness. Therapies to restore consciousness in patients with severe brain injuries have historically targeted subcortical sites in the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia, with the goal of reactivating cortical DMN nodes. However, the subcortical connectivity of the DMN has not been fully mapped, and optimal subcortical targets for therapeutic neuromodulation of consciousness have not been identified. In this work, we created a comprehensive map of DMN subcortical connectivity by combining high-resolution functional and structural datasets with advanced signal processing methods. We analyzed 7 Tesla resting-state functional MRI (rs-fMRI) data from 168 healthy volunteers acquired in the Human Connectome Project. The rs-fMRI blood-oxygen-level-dependent (BOLD) data were temporally synchronized across subjects using the BrainSync algorithm. Cortical and subcortical DMN nodes were jointly analyzed and identified at the group level by applying a novel Nadam-Accelerated SCAlable and Robust (NASCAR) tensor decomposition method to the synchronized dataset. The subcortical connectivity map was then overlaid on a 7 Tesla 100 µm ex vivo MRI dataset for neuroanatomic analysis using automated segmentation of nuclei within the brainstem, thalamus, hypothalamus, basal forebrain, and basal ganglia. We further compared the NASCAR subcortical connectivity map with its counterpart generated from canonical seed-based correlation analyses. The NASCAR method revealed that BOLD signal in the central lateral nucleus of the thalamus and ventral tegmental area of the midbrain is strongly correlated with that of the DMN. In an exploratory analysis, additional subcortical sites in the median and dorsal raphe, lateral hypothalamus, and caudate nuclei were correlated with the cortical DMN. We also found that the putamen and globus pallidus are negatively correlated (i.e., anti-correlated) with the DMN, providing rs-fMRI evidence for the mesocircuit hypothesis of human consciousness, whereby a striatopallidal feedback system modulates anterior forebrain function via disinhibition of the central thalamus. Seed-based analyses yielded similar subcortical DMN connectivity, but the NASCAR result showed stronger contrast and better spatial alignment with dopamine immunostaining data. The DMN subcortical connectivity map identified here advances understanding of the subcortical regions that contribute to human consciousness and can be used to inform the selection of therapeutic targets in clinical trials for patients with disorders of consciousness.
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Affiliation(s)
- Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - William H Curley
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Bastien Guerin
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Darin D Dougherty
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Andreas Horn
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Center for Brain Circuit Therapeutics, Department of Neurology, Brigham & Women's Hospital and Harvard Medical School, Boston, MA, USA; Department of Neurosurgery, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Movement Disorders & Neuromodulation Section, Department of Neurology, Charité - Universitätsmedizin, Berlin, Germany
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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27
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Greve DN, Billot B, Cordero D, Hoopes A, Hoffmann M, Dalca AV, Fischl B, Iglesias JE, Augustinack JC. A deep learning toolbox for automatic segmentation of subcortical limbic structures from MRI images. Neuroimage 2021; 244:118610. [PMID: 34571161 PMCID: PMC8643077 DOI: 10.1016/j.neuroimage.2021.118610] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2021] [Revised: 09/16/2021] [Accepted: 09/20/2021] [Indexed: 11/20/2022] Open
Abstract
A tool was developed to automatically segment several subcortical limbic structures (nucleus accumbens, basal forebrain, septal nuclei, hypothalamus without mammillary bodies, the mammillary bodies, and fornix) using only a T1-weighted MRI as input. This tool fills an unmet need as there are few, if any, publicly available tools to segment these clinically relevant structures. A U-Net with spatial, intensity, contrast, and noise augmentation was trained using 39 manually labeled MRI data sets. In general, the Dice scores, true positive rates, false discovery rates, and manual-automatic volume correlation were very good relative to comparable tools for other structures. A diverse data set of 698 subjects were segmented using the tool; evaluation of the resulting labelings showed that the tool failed in less than 1% of cases. Test-retest reliability of the tool was excellent. The automatically segmented volume of all structures except mammillary bodies showed effectiveness at detecting either clinical AD effects, age effects, or both. This tool will be publicly released with FreeSurfer (surfer.nmr.mgh.harvard.edu/fswiki/ScLimbic). Together with the other cortical and subcortical limbic segmentations, this tool will allow FreeSurfer to provide a comprehensive view of the limbic system in an automated way.
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Affiliation(s)
- Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA.
| | - Benjamin Billot
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Devani Cordero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Andrew Hoopes
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Malte Hoffmann
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
| | - Jean C Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Radiology Department, Boston, MA, USA
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28
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Hong S, Giese AK, Schirmer MD, Bonkhoff AK, Bretzner M, Rist P, Dalca AV, Regenhardt RW, Etherton MR, Donahue KL, Nardin M, Mocking SJT, McIntosh EC, Attia J, Benavente OR, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Roquer J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Rundek T, Sacco RL, Schmidt R, Enzinger C, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Wu O, Jern C, Lindgren AG, Maguire J, Tomppo L, Golland P, Rost NS. Excessive White Matter Hyperintensity Increases Susceptibility to Poor Functional Outcomes After Acute Ischemic Stroke. Front Neurol 2021; 12:700616. [PMID: 34566844 PMCID: PMC8461233 DOI: 10.3389/fneur.2021.700616] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2021] [Accepted: 07/28/2021] [Indexed: 11/24/2022] Open
Abstract
Objective: To personalize the prognostication of post-stroke outcome using MRI-detected cerebrovascular pathology, we sought to investigate the association between the excessive white matter hyperintensity (WMH) burden unaccounted for by the traditional stroke risk profile of individual patients and their long-term functional outcomes after a stroke. Methods: We included 890 patients who survived after an acute ischemic stroke from the MRI-Genetics Interface Exploration (MRI-GENIE) study, for whom data on vascular risk factors (VRFs), including age, sex, atrial fibrillation, diabetes mellitus, hypertension, coronary artery disease, smoking, prior stroke history, as well as acute stroke severity, 3- to-6-month modified Rankin Scale score (mRS), WMH, and brain volumes, were available. We defined the unaccounted WMH (uWMH) burden via modeling of expected WMH burden based on the VRF profile of each individual patient. The association of uWMH and mRS score was analyzed by linear regression analysis. The odds ratios of patients who achieved full functional independence (mRS < 2) in between trichotomized uWMH burden groups were calculated by pair-wise comparisons. Results: The expected WMH volume was estimated with respect to known VRFs. The uWMH burden was associated with a long-term functional outcome (β = 0.104, p < 0.01). Excessive uWMH burden significantly reduced the odds of achieving full functional independence after a stroke compared to the low and average uWMH burden [OR = 0.4, 95% CI: (0.25, 0.63), p < 0.01 and OR = 0.61, 95% CI: (0.42, 0.87), p < 0.01, respectively]. Conclusion: The excessive amount of uWMH burden unaccounted for by the traditional VRF profile was associated with worse post-stroke functional outcomes. Further studies are needed to evaluate a lifetime brain injury reflected in WMH unrelated to the VRF profile of a patient as an important factor for stroke recovery and a plausible indicator of brain health.
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Affiliation(s)
- Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Anna K. Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences & Cognition, Lille, France
| | - Pamela Rist
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, United States
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
| | - Steven J. T. Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Elissa C. McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Oscar R. Benavente
- Division of Neurology, Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - John W. Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, United States
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO, United States
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg and Department of Neurology, The Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg and Department of Neurology, The Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions M‘ediques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions M‘ediques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J. Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium
- VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Christopher R. Levi
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States
| | - James F. Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St. Louis, MO, United States
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Christian Enzinger
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
- St. Peter's and Ashford Hospitals, Egham, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Alessandro Sousa
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Tara M. Stanne
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy, University of Gothenburg and Department of Neurology, The Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia
- Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Malmo, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, United States
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Bradford B. Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, United States
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Arne G. Lindgren
- Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- School of Nursing and Midwifery, University of Technology Sydney, Sydney, NSW, Australia
| | - Liisa Tomppo
- Department of Neurology, Helsinki University Hospital and Clinical Neurosciences, University of Helsinki, Helsinki, Finland
| | - Polina Golland
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, United States
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States
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29
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Bretzner M, Bonkhoff AK, Schirmer MD, Hong S, Dalca AV, Donahue KL, Giese AK, Etherton MR, Rist PM, Nardin M, Marinescu R, Wang C, Regenhardt RW, Leclerc X, Lopes R, Benavente OR, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McArdle PF, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Wu O, Zand R, Worrall BB, Maguire JM, Lindgren A, Jern C, Golland P, Kuchcinski G, Rost NS. MRI Radiomic Signature of White Matter Hyperintensities Is Associated With Clinical Phenotypes. Front Neurosci 2021; 15:691244. [PMID: 34321995 PMCID: PMC8312571 DOI: 10.3389/fnins.2021.691244] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Accepted: 06/15/2021] [Indexed: 12/19/2022] Open
Abstract
OBJECTIVE Neuroimaging measurements of brain structural integrity are thought to be surrogates for brain health, but precise assessments require dedicated advanced image acquisitions. By means of quantitatively describing conventional images, radiomic analyses hold potential for evaluating brain health. We sought to: (1) evaluate radiomics to assess brain structural integrity by predicting white matter hyperintensities burdens (WMH) and (2) uncover associations between predictive radiomic features and clinical phenotypes. METHODS We analyzed a multi-site cohort of 4,163 acute ischemic strokes (AIS) patients with T2-FLAIR MR images with total brain and WMH segmentations. Radiomic features were extracted from normal-appearing brain tissue (brain mask-WMH mask). Radiomics-based prediction of personalized WMH burden was done using ElasticNet linear regression. We built a radiomic signature of WMH with stable selected features predictive of WMH burden and then related this signature to clinical variables using canonical correlation analysis (CCA). RESULTS Radiomic features were predictive of WMH burden (R 2 = 0.855 ± 0.011). Seven pairs of canonical variates (CV) significantly correlated the radiomics signature of WMH and clinical traits with respective canonical correlations of 0.81, 0.65, 0.42, 0.24, 0.20, 0.15, and 0.15 (FDR-corrected p-values CV 1 - 6 < 0.001, p-value CV 7 = 0.012). The clinical CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes, CV4 by hypertension, CV5 by atrial fibrillation (AF) and diabetes, CV6 by coronary artery disease (CAD), and CV7 by CAD and diabetes. CONCLUSION Radiomics extracted from T2-FLAIR images of AIS patients capture microstructural damage of the cerebral parenchyma and correlate with clinical phenotypes, suggesting different radiographical textural abnormalities per cardiovascular risk profile. Further research could evaluate radiomics to predict the progression of WMH and for the follow-up of stroke patients' brain health.
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Affiliation(s)
- Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France
| | - Anna K. Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Markus D. Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Adrian V. Dalca
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Kathleen L. Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Mark R. Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Pamela M. Rist
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- Division of Preventive Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, United States
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Razvan Marinescu
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Clinton Wang
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Robert W. Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
| | - Xavier Leclerc
- Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France
| | - Renaud Lopes
- Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France
- CNRS, Institut Pasteur de Lille, US 41 - UMS 2014 - PLBS, Lille, France
| | - Oscar R. Benavente
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - John W. Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Brazil
| | - Christoph J. Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, United States
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Division of Emergency Medicine, Washington University School of Medicine, St. Louis, MO, United States
- Department of Neurology, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO, United States
| | - Lukas Holmegaard
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Katarina Jood
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), Institut Hospital del Mar d’Investigacions Mèdiques (IMIM), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J. Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven – University of Leuven, Leuven, Belgium
- VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Christopher R. Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Patrick F. McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, United States
| | - James F. Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine and Barnes-Jewish Hospital, St. Louis, MO, United States
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
| | - Jonathan Rosand
- Henry and Allison McCance Center for Brain Health, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Jaume Roquer
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Ralph L. Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University of Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom
- Ashford and St. Peter’s Hospitals, Chertsey and Ashford, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Alessandro Sousa
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Tara M. Stanne
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinica Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Department of Neurology Austin Health, Heidelberg, VIC, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Ona Wu
- A. A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, United States
| | - Bradford B. Worrall
- Department of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Jane M. Maguire
- Faculty of Health, University of Technology Sydney, Ultimo, NSW, Australia
| | - Arne Lindgren
- Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Christina Jern
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Grégory Kuchcinski
- Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences and Cognition, University of Lille, Lille, France
| | - Natalia S. Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Boston, MA, United States
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30
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Bonkhoff AK, Schirmer MD, Bretzner M, Hong S, Regenhardt RW, Brudfors M, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Hancock BL, Mocking SJT, McIntosh EC, Attia J, Benavente OR, Bevan S, Cole JW, Donatti A, Griessenauer CJ, Heitsch L, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Levi CR, McDonough CW, Meschia JF, Phuah CL, Rolfs A, Ropele S, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Söderholm M, Sousa A, Stanne TM, Strbian D, Tatlisumak T, Thijs V, Vagal A, Wasselius J, Woo D, Zand R, McArdle PF, Worrall BB, Jern C, Lindgren AG, Maguire J, Bzdok D, Wu O, Rost NS. Outcome after acute ischemic stroke is linked to sex-specific lesion patterns. Nat Commun 2021; 12:3289. [PMID: 34078897 PMCID: PMC8172535 DOI: 10.1038/s41467-021-23492-3] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 04/30/2021] [Indexed: 01/31/2023] Open
Abstract
Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical stroke lesions and designed a Bayesian hierarchical modeling framework tailored to estimate possible sex differences in lesion patterns linked to acute stroke severity (National Institute of Health Stroke Scale). This framework was developed in 555 patients (38% female). Findings were validated in an independent cohort (n = 503, 41% female). Here, we show brain lesions in regions subserving motor and language functions help explain stroke severity in both men and women, however more widespread lesion patterns are relevant in female patients. Higher stroke severity in women, but not men, is associated with left hemisphere lesions in the vicinity of the posterior circulation. Our results suggest there are sex-specific functional cerebral asymmetries that may be important for future investigations of sex-stratified approaches to management of acute ischemic stroke.
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Affiliation(s)
- Anna K Bonkhoff
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Clinic for Neuroradiology, University Hospital Bonn, Bonn, Germany
| | - Martin Bretzner
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Univ. Lille, Inserm, CHU Lille, U1171 - LilNCog (JPARC) - Lille Neurosciences & Cognition, F-59000, Lille, France
| | - Sungmin Hong
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Robert W Regenhardt
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mikael Brudfors
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J Nardin
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Anne-Katrin Giese
- Department of Neurology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brandon L Hancock
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - John Attia
- Hunter Medical Research Institute, Newcastle, NSW, Australia
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
| | - Oscar R Benavente
- Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
| | - Stephen Bevan
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Amanda Donatti
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Christoph J Griessenauer
- Department of Neurosurgery, Geisinger, Danville, PA, USA
- Research Institute of Neurointervention, Paracelsus Medical University, Salzburg, Austria
| | - Laura Heitsch
- Department of Emergency Medicine, Washington University School of Medicine, St Louis, MO, USA
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | - Lukas Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Katarina Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Robin Lemmens
- KU Leuven - University of Leuven, Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium
- VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Leuven, Belgium
| | - Christopher R Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, NSW, Australia
- Department of Neurology, John Hunter Hospital, Newcastle, NSW, Australia
| | - Caitrin W McDonough
- Department of Pharmacotherapy and Translational Research and Center for Pharmacogenomics, University of Florida, Gainesville, FL, USA
| | | | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine & Barnes-Jewish Hospital, St Louis, MO, USA
| | | | - Stefan Ropele
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Jonathan Rosand
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, UK
- St Peter's and Ashford Hospitals, Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Martin Söderholm
- Department of clinical sciences Malmö, Lund University, Lund, Sweden
- Department of Neurology, Skåne University Hospital, Lund and Malmö, Sweden
| | - Alessandro Sousa
- School of Medical Sciences, University of Campinas (UNICAMP) and the Brazilian Institute of Neuroscience and Neurotechnology (BRAINN), Campinas, Sao Paulo, Brazil
| | - Tara M Stanne
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Daniel Strbian
- Department of Neurology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Ramin Zand
- Department of Neurology, Geisinger, Danville, PA, USA
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Clinical Genetics and Genomics, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Arne G Lindgren
- Department of Neurology, Skåne University Hospital, Lund, Sweden
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, NSW, Australia
| | - Danilo Bzdok
- Department of Biomedical Engineering, McConnell Brain Imaging Centre, Montreal Neurological Institute, Faculty of Medicine, School of Computer Science, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Sederevičius D, Vidal-Piñeiro D, Sørensen Ø, van Leemput K, Iglesias JE, Dalca AV, Greve DN, Fischl B, Bjørnerud A, Walhovd KB, Fjell AM. Reliability and sensitivity of two whole-brain segmentation approaches included in FreeSurfer - ASEG and SAMSEG. Neuroimage 2021; 237:118113. [PMID: 33940143 PMCID: PMC9052126 DOI: 10.1016/j.neuroimage.2021.118113] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Revised: 03/19/2021] [Accepted: 04/15/2021] [Indexed: 11/26/2022] Open
Abstract
Accurate and reliable whole-brain segmentation is critical to longitudinal neuroimaging studies. We undertake a comparative analysis of two subcortical segmentation methods, Automatic Segmentation (ASEG) and Sequence Adaptive Multimodal Segmentation (SAMSEG), recently provided in the open-source neuroimaging package FreeSurfer 7.1, with regard to reliability, bias, sensitivity to detect longitudinal change, and diagnostic sensitivity to Alzheimer’s disease. First, we assess intra- and inter-scanner reliability for eight bilateral subcortical structures: amygdala, caudate, hippocampus, lateral ventricles, nucleus accumbens, pallidum, putamen and thalamus. For intra-scanner analysis we use a large sample of participants (n = 1629) distributed across the lifespan (age range = 4–93 years) and acquired on a 1.5T Siemens Avanto (n = 774) and a 3T Siemens Skyra (n = 855) scanners. For inter-scanner analysis we use a sample of 24 participants scanned on the day with three models of Siemens scanners: 1.5T Avanto, 3T Skyra and 3T Prisma. Second, we test how each method detects volumetric age change using longitudinal follow up scans (n = 491 for Avanto and n = 245 for Skyra; interscan interval = 1–10 years). Finally, we test sensitivity to clinically relevant change. We compare annual rate of hippocampal atrophy in cognitively normal older adults (n = 20), patients with mild cognitive impairment (n = 20) and Alzheimer’s disease (n = 20). We find that both ASEG and SAMSEG are reliable and lead to the detection of within-person longitudinal change, although with notable differences between age-trajectories for most structures, including hippocampus and amygdala. In summary, SAMSEG yields significantly lower differences between repeated measures for intra- and inter-scanner analysis without compromising sensitivity to changes and demonstrating ability to detect clinically relevant longitudinal changes.
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Affiliation(s)
- Donatas Sederevičius
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Pb. 1094, Blindern, Oslo 0317, Norway.
| | - Didac Vidal-Piñeiro
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Pb. 1094, Blindern, Oslo 0317, Norway
| | - Øystein Sørensen
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Pb. 1094, Blindern, Oslo 0317, Norway
| | - Koen van Leemput
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States; Department of Health Technology, Technical University of Denmark, Denmark
| | - Juan Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States; Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, MIT, United States
| | - Adrian V Dalca
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States; Computer Science and Artificial Intelligence Laboratory, MIT, United States
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, United States; Computer Science and Artificial Intelligence Laboratory, MIT, United States
| | - Atle Bjørnerud
- Division of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Kristine B Walhovd
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Pb. 1094, Blindern, Oslo 0317, Norway; Division of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
| | - Anders M Fjell
- Center for Lifespan Changes in Brain and Cognition, University of Oslo, Pb. 1094, Blindern, Oslo 0317, Norway; Division of Radiology and Nuclear Medicine, Oslo University Hospital, Norway
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32
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Abstract
We introduce a strategy for learning image registration without acquired imaging data, producing powerful networks agnostic to magnetic resonance imaging (MRI) contrast. While classical methods accurately estimate the spatial correspondence between images, they solve an optimization problem for every new image pair. Learning methods are fast at test time but limited to images with contrasts and geometric content similar to those seen during training. We propose to remove this dependency using a generative strategy that exposes networks to a wide range of images synthesized from segmentations during training, forcing them to generalize across contrasts. We show that networks trained within this framework generalize to a broad array of unseen MRI contrasts and surpass classical state-of-the-art brain registration accuracy by up to 12.4 Dice points for a variety of tested contrast combinations. Critically, training on arbitrary shapes synthesized from noise distributions results in competitive performance, removing the dependency on acquired data of any kind. Additionally, since anatomical label maps are often available for the anatomy of interest, we show that synthesizing images from these dramatically boosts performance, while still avoiding the need for real intensity images during training.
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Affiliation(s)
- Malte Hoffmann
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
| | - Benjamin Billot
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
| | - Juan E Iglesias
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Centre for Medical Image Computing, University College London, WC1E 6BT, UK
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
| | - Adrian V Dalca
- Athinoula A. Martinos Center, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02115, USA
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA 02139, USA
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33
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Billot B, Cerri S, Van Leemput K, Dalca AV, Iglesias JE. JOINT SEGMENTATION OF MULTIPLE SCLEROSIS LESIONS AND BRAIN ANATOMY IN MRI SCANS OF ANY CONTRAST AND RESOLUTION WITH CNNs. Proc IEEE Int Symp Biomed Imaging 2021; 2021:1971-1974. [PMID: 34367472 PMCID: PMC8340983 DOI: 10.1109/isbi48211.2021.9434127] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
We present the first deep learning method to segment Multiple Sclerosis lesions and brain structures from MRI scans of any (possibly multimodal) contrast and resolution. Our method only requires segmentations to be trained (no images), as it leverages the generative model of Bayesian segmentation to generate synthetic scans with simulated lesions, which are then used to train a CNN. Our method can be retrained to segment at any resolution by adjusting the amount of synthesised partial volume. By construction, the synthetic scans are perfectly aligned with their labels, which enables training with noisy labels obtained with automatic methods. The training data are generated on the fly, and aggressive augmentation (including artefacts) is applied for improved generalisation. We demonstrate our method on two public datasets, comparing it with a state-of-the-art Bayesian approach implemented in FreeSurfer, and dataset specific CNNs trained on real data. The code is available at https://github.com/BBillot/SynthSeg.
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Affiliation(s)
- Benjamin Billot
- Center for Medical Image Computing, University College London, UK
| | - Stefano Cerri
- Department of Health Technology, Technical University of Denmark, Denmark
| | - Koen Van Leemput
- Department of Health Technology, Technical University of Denmark, Denmark
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, UK
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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34
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Hong S, Giese AK, Schirmer MD, Dalca AV, Bonkhoff A, Bretzner M, Donahue K, Jern C, Lindgren A, Maguire J, Rost NS. Abstract MP16: Excessive White Matter Hyperintensity Burden and Functional Outcomes After Acute Ischemic Stroke. Stroke 2021. [DOI: 10.1161/str.52.suppl_1.mp16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Objective:
Ability of the brain to recover after an acute ischemic stroke (AIS) is linked to the pre-stroke burden of white matter hyperintensity (WMH), a radiographic marker of brain health. We sought to determine the excessive WMH burden in an AIS population and investigate its association with 3-month stroke outcomes.
Data:
We used 2,435 subjects from the MRI-GENIE study. Three-month functional outcomes of 872 subjects among those subjects were measured by 90-day modified Ranking Scale (mRS).
Methods:
We automatically quantified WMH volume (WMHv) on FLAIR images and adjusted for a brain volume. We modeled a trend using the factor analysis (FA) log-linear regression using age, sex, atrial fibrillation, diabetes, hypertension, coronary artery disease and smoking as input variables. We categorized three WMH burden groups based on the conditional probability given by the model (LOW: lower 33%, MED: middle 34%, and HIGH: upper 33%). The subgroups were compared with respect to mRS (median and dichotomized odds ratio (OR) (good/poor: mRS 0-2/3-6)).
Results:
Five FA components out of seven with significant relationship to WMHv (p<0.001) were used for the regression modeling (R
2
=0.359). The HIGH group showed higher median (median=2, IQR=2) mRS score than LOW (median=1, IQR=1) and MED (median=1, IQR=1). The odds (OR) of good AIS outcome for LOW and MED were 1.8 (p=0.0001) and 1.6 (p=0.006) times higher than HIGH, respectively.
Conclusion:
Once accounted for clinical covariates, the excessive WMHv was associated with worse 3-month stroke outcomes. These data suggest that a life-time of injury to the white matter reflected in WMH is an important factor for stroke recovery and an indicator of the brain health.
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Affiliation(s)
- Sungmin Hong
- Neurology, Massachusetts General Hosp, Boston, MA
| | | | | | | | | | | | | | - Christina Jern
- Laboratory Medicine, Sahlgrenska Academy, Univ of Gothenburg, Gothenburg, Sweden
| | - Arne Lindgren
- Clinical Sciences and Neurology, Lund Univ, Lund, Sweden
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Bretzner M, Bonkhoff A, Schirmer MD, Hong S, Dalca AV, Donahue KL, Giese AK, Etherton MR, Nardin M, Leclerc X, Kuchcinski G, Rost NS. Abstract 10: Radiomic Signature of the White Matter Hyperintensity Burden Correlates With Clinical Phenotypes. Stroke 2021. [DOI: 10.1161/str.52.suppl_1.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Introduction:
Structural integrity of cerebral parenchyma is an essential radiographic equivalent of brain health; but its assessment usually requires dedicated advanced image acquisitions. Radiomics analyses bear the potential to describe radiophenotypes beyond what meets the naked eye. We sought to: 1) evaluate this novel approach to predict white matter hyperintensity (WMH) burden and 2) uncover latent clinico-radiological associations.
Methods:
An international, multi-site cohort of 4,164 acute ischemic stroke (AIS) patients with FLAIR MRI (MRI-GENIE study) underwent total brain and WMH lesion segmentation using convolutional neural networks. Radiomic features (n=1905) were extracted from clinical FLAIR images outside of the WMH (brain mask - WMH mask). Prediction of the WMH burden using radiomics was done using LASSO regression. Radiomic signature of WMH was built with the most stable selected features, then compared to the clinical variables using canonical correlation analysis.
Results:
In this cohort, (mean age=62.8±15.0, median WMH volume=4.2cc IQR 1.4-11.2), radiomic features were highly predictive of WMH burden (R2=0.8±0.012). Radiomic signature of WMH included 68 features. All 7 pairs of extracted canonical variates (CV) were statistically significant with respective canonical correlations of 0.79, 0.64, 0.44, 0.21, 0.16, 0.15 (Bonferroni corrected p-values
CV1-6
<.001, p-value
CV7
=.003). Upon examination, CV1 was mainly influenced by age, CV2 by sex, CV3 by history of smoking and diabetes mellitus (DM), CV4 by hypertension, CV5 by atrial fibrillation (AF) and DM, CV6 by coronary artery disease (CAD) and CV7 by CAD and DM.
Conclusion:
Radiomics extracted from clinical grade FLAIR images of AIS patients seem able to capture structural integrity of the cerebral parenchyma and to correlate with clinical phenotypes. Further research could evaluate radiomics to predict the progression of cerebral small vessel disease on longitudinal data.
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Affiliation(s)
- Martin Bretzner
- J. Philip Kistler Stroke Rsch Cntr, Massachusetts General Hosp, Boston, MA
| | | | | | | | | | | | | | | | | | - Xavier Leclerc
- Neuroradiology, Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences & Cognition, Lille, France
| | - Grégory Kuchcinski
- Neuroradiology, Univ. Lille, Inserm, CHU Lille, U1172 - LilNCog (JPARC) - Lille Neurosciences & Cognition, Lille, France
| | - Natalia S Rost
- Neurology, Massachusetts General Hosp, Boston, Boston, MA
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36
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Abstract
Non-rigid cortical registration is an important and challenging task due to the geometric complexity of the human cortex and the high degree of inter-subject variability. A conventional solution is to use a spherical representation of surface properties and perform registration by aligning cortical folding patterns in that space. This strategy produces accurate spatial alignment, but often requires high computational cost. Recently, convolutional neural networks (CNNs) have demonstrated the potential to dramatically speed up volumetric registration. However, due to distortions introduced by projecting a sphere to a 2D plane, a direct application of recent learning-based methods to surfaces yields poor results. In this study, we present SphereMorph, a diffeomorphic registration framework for cortical surfaces using deep networks that addresses these issues. SphereMorph uses a UNet-style network associated with a spherical kernel to learn the displacement field and warps the sphere using a modified spatial transformer layer. We propose a resampling weight in computing the data fitting loss to account for distortions introduced by polar projection, and demonstrate the performance of our proposed method on two tasks, including cortical parcellation and group-wise functional area alignment. The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency. The source code of SphereMorph will be released to the public upon acceptance of this manuscript at https://github.com/voxelmorph/spheremorph.
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Affiliation(s)
- Jieyu Cheng
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA
| | - Adrian V Dalca
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Bruce Fischl
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Lilla Zöllei
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
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37
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Drake M, Frid P, Hansen BM, Wu O, Giese AK, Schirmer MD, Donahue K, Cloonan L, Irie RE, Bouts MJRJ, McIntosh EC, Mocking SJT, Dalca AV, Sridharan R, Xu H, Giralt-Steinhauer E, Holmegaard L, Jood K, Roquer J, Cole JW, McArdle PF, Broderick JP, Jiménez-Conde J, Jern C, Kissela BM, Kleindorfer DO, Lemmens R, Meschia JF, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Worrall BB, Kittner SJ, Mitchell BD, Rosand J, Golland P, Lindgren A, Rost NS, Wassélius J. Diffusion-Weighted Imaging, MR Angiography, and Baseline Data in a Systematic Multicenter Analysis of 3,301 MRI Scans of Ischemic Stroke Patients-Neuroradiological Review Within the MRI-GENIE Study. Front Neurol 2020; 11:577. [PMID: 32670186 PMCID: PMC7330135 DOI: 10.3389/fneur.2020.00577] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2019] [Accepted: 05/19/2020] [Indexed: 11/13/2022] Open
Abstract
Background: Magnetic resonance imaging (MRI) serves as a cornerstone in defining stroke phenotype and etiological subtype through examination of ischemic stroke lesion appearance and is therefore an essential tool in linking genetic traits and stroke. Building on baseline MRI examinations from the centralized and structured radiological assessments of ischemic stroke patients in the Stroke Genetics Network, the results of the MRI-Genetics Interface Exploration (MRI-GENIE) study are described in this work. Methods: The MRI-GENIE study included patients with symptoms caused by ischemic stroke (N = 3,301) from 12 international centers. We established and used a structured reporting protocol for all assessments. Two neuroradiologists, using a blinded evaluation protocol, independently reviewed the baseline diffusion-weighted images (DWIs) and magnetic resonance angiography images to determine acute lesion and vascular occlusion characteristics. Results: In this systematic multicenter radiological analysis of clinical MRI from 3,301 acute ischemic stroke patients according to a structured prespecified protocol, we identified that anterior circulation infarcts were most prevalent (67.4%), that infarcts in the middle cerebral artery (MCA) territory were the most common, and that the majority of large artery occlusions 0 to 48 h from ictus were in the MCA territory. Multiple acute lesions in one or several vascular territories were common (11%). Of 2,238 patients with unilateral DWI lesions, 52.6% had left-sided infarct lateralization (P = 0.013 for χ2 test). Conclusions: This large-scale analysis of a multicenter MRI-based cohort of AIS patients presents a unique imaging framework facilitating the relationship between imaging and genetics for advancing the knowledge of genetic traits linked to ischemic stroke.
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Affiliation(s)
- Mattias Drake
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Department of Neurology and Rehabilitation Medicine, Neurology, Skåne University Hospital, Malmö, Sweden
| | - Björn M Hansen
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - Ona Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States
| | - Anne-Katrin Giese
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, United States
| | - Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Kathleen Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Lisa Cloonan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Robert E Irie
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States
| | - Mark J R J Bouts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States
| | - Elissa C McIntosh
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States
| | - Steven J T Mocking
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States
| | - Adrian V Dalca
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States.,Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Ramesh Sridharan
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Eva Giralt-Steinhauer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), University at Autonoma de Barcelona, Barcelona, Spain
| | - Lukas Holmegaard
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Katarina Jood
- Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), University at Autonoma de Barcelona, Barcelona, Spain
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States
| | - Joseph P Broderick
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Jordi Jiménez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), University at Autonoma de Barcelona, Barcelona, Spain
| | - Christina Jern
- Department of Laboratory Medicine, Institute of Biomedicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Brett M Kissela
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Dawn O Kleindorfer
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), Leuven, Belgium.,Department of Neurology, VIB, Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Leuven, Belgium
| | - James F Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, United States
| | - Tatjana Rundek
- Department of Neurology and the Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Ralph L Sacco
- Department of Neurology and the Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, United States
| | - Reinhold Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, United Kingdom.,Ashford and St Peter's Hospital, Surrey, United Kingdom
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia.,Department of Neurology, Austin Health, Heidelberg, VIC, Australia
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, United States
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, United States
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, United States
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.,Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, United States
| | - Jonathan Rosand
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, United States.,Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Henry and Allison McCancer Center for Brain Health and Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, United States
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, United States
| | - Arne Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.,Department of Neurology and Rehabilitation Medicine, Neurology, Skåne University Hospital, Lund, Sweden
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Johan Wassélius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.,Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
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Dubost F, Bruijne MD, Nardin M, Dalca AV, Donahue KL, Giese AK, Etherton MR, Wu O, Groot MD, Niessen W, Vernooij M, Rost NS, Schirmer MD. Multi-atlas image registration of clinical data with automated quality assessment using ventricle segmentation. Med Image Anal 2020; 63:101698. [PMID: 32339896 PMCID: PMC7275913 DOI: 10.1016/j.media.2020.101698] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2019] [Revised: 12/03/2019] [Accepted: 04/06/2020] [Indexed: 02/08/2023]
Abstract
Registration is a core component of many imaging pipelines. In case of clinical scans, with lower resolution and sometimes substantial motion artifacts, registration can produce poor results. Visual assessment of registration quality in large clinical datasets is inefficient. In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain. The method consists of automatically segmenting the ventricles of a given scan using a neural network, and comparing the segmentation to the atlas ventricles propagated to image space. We used the proposed method to improve clinical image registration to a general atlas by computing multiple registrations - one directly to the general atlas and others via different age-specific atlases - and then selecting the registration that yielded the highest ventricle overlap. Finally, as an example application of the complete pipeline, a voxelwise map of white matter hyperintensity burden was computed using only the scans with registration quality above a predefined threshold. Methods were evaluated in a single-site dataset of more than 1000 scans, as well as a multi-center dataset comprising 142 clinical scans from 12 sites. The automated ventricle segmentation reached a Dice coefficient with manual annotations of 0.89 in the single-site dataset, and 0.83 in the multi-center dataset. Registration via age-specific atlases could improve ventricle overlap compared to a direct registration to the general atlas (Dice similarity coefficient increase up to 0.15). Experiments also showed that selecting scans with the registration quality assessment method could improve the quality of average maps of white matter hyperintensity burden, instead of using all scans for the computation of the white matter hyperintensity map. In this work, we demonstrated the utility of an automated tool for assessing image registration quality in clinical scans. This image quality assessment step could ultimately assist in the translation of automated neuroimaging pipelines to the clinic.
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Affiliation(s)
- Florian Dubost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands.
| | - Marleen de Bruijne
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
| | - Marco Nardin
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA
| | - Kathleen L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Anne-Katrin Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Mark R Etherton
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Marius de Groot
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Wiro Niessen
- Biomedical Imaging Group Rotterdam, Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, The Netherlands; Department of Imaging Physics, Faculty of Applied Science, TU Delft, Delft, The Netherlands
| | - Meike Vernooij
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, the Netherlands; Department of Epidemiology, Erasmus MC - University Medical Center Rotterdam, the Netherlands
| | - Natalia S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Markus D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
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39
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Giese AK, Schirmer MD, Dalca AV, Sridharan R, Donahue KL, Nardin M, Irie R, McIntosh EC, Mocking SJT, Xu H, Cole JW, Giralt-Steinhauer E, Jimenez-Conde J, Jern C, Kleindorfer DO, Lemmens R, Wasselius J, Lindgren A, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Worrall BB, Woo D, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Meschia JF, Wu O, Golland P, Rost NS. White matter hyperintensity burden in acute stroke patients differs by ischemic stroke subtype. Neurology 2020; 95:e79-e88. [PMID: 32493718 DOI: 10.1212/wnl.0000000000009728] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2019] [Accepted: 12/12/2019] [Indexed: 01/04/2023] Open
Abstract
OBJECTIVE To examine etiologic stroke subtypes and vascular risk factor profiles and their association with white matter hyperintensity (WMH) burden in patients hospitalized for acute ischemic stroke (AIS). METHODS For the MRI Genetics Interface Exploration (MRI-GENIE) study, we systematically assembled brain imaging and phenotypic data for 3,301 patients with AIS. All cases underwent standardized web tool-based stroke subtyping with the Causative Classification of Ischemic Stroke (CCS). WMH volume (WMHv) was measured on T2 brain MRI scans of 2,529 patients with a fully automated deep-learning trained algorithm. Univariable and multivariable linear mixed-effects modeling was carried out to investigate the relationship of vascular risk factors with WMHv and CCS subtypes. RESULTS Patients with AIS with large artery atherosclerosis, major cardioembolic stroke, small artery occlusion (SAO), other, and undetermined causes of AIS differed significantly in their vascular risk factor profile (all p < 0.001). Median WMHv in all patients with AIS was 5.86 cm3 (interquartile range 2.18-14.61 cm3) and differed significantly across CCS subtypes (p < 0.0001). In multivariable analysis, age, hypertension, prior stroke, smoking (all p < 0.001), and diabetes mellitus (p = 0.041) were independent predictors of WMHv. When adjusted for confounders, patients with SAO had significantly higher WMHv compared to those with all other stroke subtypes (p < 0.001). CONCLUSION In this international multicenter, hospital-based cohort of patients with AIS, we demonstrate that vascular risk factor profiles and extent of WMH burden differ by CCS subtype, with the highest lesion burden detected in patients with SAO. These findings further support the small vessel hypothesis of WMH lesions detected on brain MRI of patients with ischemic stroke.
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Affiliation(s)
- Anne-Katrin Giese
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Markus D Schirmer
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Adrian V Dalca
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Ramesh Sridharan
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Kathleen L Donahue
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Marco Nardin
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Robert Irie
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Elissa C McIntosh
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Steven J T Mocking
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Huichun Xu
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - John W Cole
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Eva Giralt-Steinhauer
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Jordi Jimenez-Conde
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Christina Jern
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Dawn O Kleindorfer
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Robin Lemmens
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Johan Wasselius
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Arne Lindgren
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Tatjana Rundek
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Ralph L Sacco
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Reinhold Schmidt
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Pankaj Sharma
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Agnieszka Slowik
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Vincent Thijs
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Bradford B Worrall
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Daniel Woo
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Steven J Kittner
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Patrick F McArdle
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Braxton D Mitchell
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Jonathan Rosand
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - James F Meschia
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Ona Wu
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
| | - Polina Golland
- From the Department of Neurology (A.-K.G., M.D.S., K.L.D., M.N., J.R., O.W., N.S.R.), Massachusetts General Hospital, Harvard Medical School, Boston; Program in Medical and Population Genetics (A.K.-G, J.R.), Broad Institute of MIT and Harvard; Computer Science and Artificial Intelligence Lab (M.D.S., A.V.D., R. Sridharan, P.G.), Massachusetts Institute of Technology, Cambridge; Department of Population Health Sciences (M.D.S.), German Centre for Neurodegenerative Diseases, Bonn, Germany; Athinoula A. Martinos Center for Biomedical Imaging (A.V.D., R.I., E.C.M., S.J.T.M., J.R., O.W.), Department of Radiology, Massachusetts General Hospital, Charlestown; Division of Endocrinology, Diabetes and Nutrition (H.X., P.F.M., B.D.M.), Department of Medicine, University of Maryland School of Medicine; Department of Neurology (J.W.C., S.J.K.), University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore; Department of Neurology (E.G.-S., J.J.-C.), Neurovascular Research Group, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Spain; Institute of Biomedicine (C.J.), Sahlgrenska Academy at University of Gothenburg, Sweden; Department of Neurology and Rehabilitation Medicine (D.O.K., D.W.), University of Cincinnati College of Medicine, OH; KU Leuven-University of Leuven (R.L.), Department of Neurosciences, Experimental Neurology; VIB (R.L.), Vesalius Research Center, Laboratory of Neurobiology, University Hospitals Leuven, Department of Neurology, Belgium; Department of Clinical Sciences Lund (J.W., A.L.), Neurology, Lund University; Department of Neurology and Rehabilitation Medicine (A.L.), Neurology, Skåne University Hospital, Lund, Sweden; Department of Neurology (T.R., R.L.S.), Miller School of Medicine, University of Miami, The Evelyn F. McKnight Brain Institute, FL; Department of Neurology (R. Schmidt), Clinical Division of Neurogeriatrics, Medical University Graz, Austria; Institute of Cardiovascular Research (P.S.), Royal Holloway University of London, Egham, UK; Ashford and St Peter's Hospital (P.S.), UK; Department of Neurology (A.S.), Jagiellonian University Medical College, Krakow, Poland; Stroke Division (V.T.), Florey Institute of Neuroscience and Mental Health, University of Melbourne Heidelberg; Department of Neurology (V.T.), Austin Health, Heidelberg, Victoria, Australia; Departments of Neurology and Public Health Sciences (B.B.W.), University of Virginia, Charlottesville; Center for Genomic Medicine (J.R.), Massachusetts General Hospital; Henry and Allison McCance Center for Brain Health (J.R.), Boston, MA; and Department of Neurology (J.F.M.), Mayo Clinic, Jacksonville, FL
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Schirmer MD, Donahue KL, Nardin MJ, Dalca AV, Giese AK, Etherton MR, Mocking SJT, McIntosh EC, Cole JW, Holmegaard L, Jood K, Jimenez-Conde J, Kittner SJ, Lemmens R, Meschia JF, Rosand J, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Stanne TM, Vagal A, Wasselius J, Woo D, Bevan S, Heitsch L, Phuah CL, Strbian D, Tatlisumak T, Levi CR, Attia J, McArdle PF, Worrall BB, Wu O, Jern C, Lindgren A, Maguire J, Thijs V, Rost NS. Brain Volume: An Important Determinant of Functional Outcome After Acute Ischemic Stroke. Mayo Clin Proc 2020; 95:955-965. [PMID: 32370856 DOI: 10.1016/j.mayocp.2020.01.027] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/11/2019] [Revised: 12/16/2019] [Accepted: 01/08/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE To determine whether brain volume is associated with functional outcome after acute ischemic stroke (AIS). PATIENTS AND METHODS This study was conducted between July 1, 2014, and March 16, 2019. We analyzed cross-sectional data of the multisite, international hospital-based MRI-Genetics Interface Exploration study with clinical brain magnetic resonance imaging obtained on admission for index stroke and functional outcome assessment. Poststroke outcome was determined using the modified Rankin Scale score (0-6; 0 = asymptomatic; 6 = death) recorded between 60 and 190 days after stroke. Demographic characteristics and other clinical variables including acute stroke severity (measured as National Institutes of Health Stroke Scale score), vascular risk factors, and etiologic stroke subtypes (Causative Classification of Stroke system) were recorded during index admission. RESULTS Utilizing the data from 912 patients with AIS (mean ± SD age, 65.3±14.5 years; male, 532 [58.3%]; history of smoking, 519 [56.9%]; hypertension, 595 [65.2%]) in a generalized linear model, brain volume (per 155.1 cm3) was associated with age (β -0.3 [per 14.4 years]), male sex (β 1.0), and prior stroke (β -0.2). In the multivariable outcome model, brain volume was an independent predictor of modified Rankin Scale score (β -0.233), with reduced odds of worse long-term functional outcomes (odds ratio, 0.8; 95% CI, 0.7-0.9) in those with larger brain volumes. CONCLUSION Larger brain volume quantified on clinical magnetic resonance imaging of patients with AIS at the time of stroke purports a protective mechanism. The role of brain volume as a prognostic, protective biomarker has the potential to forge new areas of research and advance current knowledge of the mechanisms of poststroke recovery.
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Affiliation(s)
- Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston; Department of Population Health Sciences, German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.
| | - Kathleen L Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston
| | - Marco J Nardin
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Anne-Katrin Giese
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston
| | - Mark R Etherton
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD; Veterans Affairs Maryland Health Care System, Baltimore, MD
| | - Lukas Holmegaard
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Katarina Jood
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group, Institut Hospital del Mar d'Investigacions Mèdiques, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine, Baltimore, MD; Veterans Affairs Maryland Health Care System, Baltimore, MD
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease, KU Leuven-University of Leuven, Flemish Institute for Biotechnology, Vesalius Research Center, Laboratory of Neurobiology, and Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | | | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston; Center for Genomic Medicine, Massachusetts General Hospital, Boston; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group, Institut Hospital del Mar d'Investigacions Mèdiques, Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL
| | - Ralph L Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, UK, and St Peter's and Ashford Hospitals Foundation Trust, Chertsey, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Tara M Stanne
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Johan Wasselius
- Department of Clinical Sciences, Radiology, Lund University, Lund, Sweden; Department of Radiology, Division of Neuroradiology, Skåne University Hospital, Malmö, Sweden
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH
| | - Stephen Bevan
- School of Life Sciences, University of Lincoln, Lincoln, UK
| | - Laura Heitsch
- Division of Emergency Medicine, Washington University School of Medicine, St Louis, MO
| | - Chia-Ling Phuah
- Department of Neurology, Washington University School of Medicine, St Louis, MO; Barnes-Jewish Hospital, St Louis, MO
| | - Daniel Strbian
- Division of Neurocritical Care and Emergency Neurology, Department of Neurology, Helsinki University Central Hospital, Helsinki, Finland
| | - Turgut Tatlisumak
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden; Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Christopher R Levi
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia; Department of Neurology, John Hunter Hospital, Newcastle, New South Wales, Australia
| | - John Attia
- School of Medicine and Public Health, University of Newcastle, Newcastle, New South Wales, Australia; Hunter Medical Research Institute, Newcastle, New South Wales, Australia
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD
| | - Bradford B Worrall
- Department of Neurology and Department of Public Health Sciences, University of Virginia, Charlottesville, VA
| | - Ona Wu
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown
| | - Christina Jern
- Institute of Biomedicine, Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Arne Lindgren
- Department of Neurology, Lund University, Lund, Sweden; Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
| | - Jane Maguire
- University of Technology Sydney, Sydney, Australia
| | - Vincent Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health and Department of Neurology, Austin Health, Heidelberg, Australia
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts General Hospital, Boston
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Frid P, Drake M, Giese AK, Wasselius J, Schirmer MD, Donahue KL, Cloonan L, Irie R, Bouts MJRJ, McIntosh EC, Mocking SJT, Dalca AV, Sridharan R, Xu H, Giralt-Steinhauer E, Holmegaard L, Jood K, Roquer J, Cole JW, McArdle PF, Broderick JP, Jimenez-Conde J, Jern C, Kissela BM, Kleindorfer DO, Lemmens R, Meschia JF, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Worrall BB, Kittner SJ, Mitchell BD, Petersson J, Rosand J, Golland P, Wu O, Rost NS, Lindgren A. Detailed phenotyping of posterior vs. anterior circulation ischemic stroke: a multi-center MRI study. J Neurol 2020; 267:649-658. [PMID: 31709475 PMCID: PMC7035231 DOI: 10.1007/s00415-019-09613-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2019] [Revised: 10/28/2019] [Accepted: 10/30/2019] [Indexed: 02/06/2023]
Abstract
OBJECTIVE Posterior circulation ischemic stroke (PCiS) constitutes 20-30% of ischemic stroke cases. Detailed information about differences between PCiS and anterior circulation ischemic stroke (ACiS) remains scarce. Such information might guide clinical decision making and prevention strategies. We studied risk factors and ischemic stroke subtypes in PCiS vs. ACiS and lesion location on magnetic resonance imaging (MRI) in PCiS. METHODS Out of 3,301 MRIs from 12 sites in the National Institute of Neurological Disorders and Stroke (NINDS) Stroke Genetics Network (SiGN), we included 2,381 cases with acute DWI lesions. The definition of ACiS or PCiS was based on lesion location. We compared the groups using Chi-squared and logistic regression. RESULTS PCiS occurred in 718 (30%) patients and ACiS in 1663 (70%). Diabetes and male sex were more common in PCiS vs. ACiS (diabetes 27% vs. 23%, p < 0.05; male sex 68% vs. 58%, p < 0.001). Both were independently associated with PCiS (diabetes, OR = 1.29; 95% CI 1.04-1.61; male sex, OR = 1.46; 95% CI 1.21-1.78). ACiS more commonly had large artery atherosclerosis (25% vs. 20%, p < 0.01) and cardioembolic mechanisms (17% vs. 11%, p < 0.001) compared to PCiS. Small artery occlusion was more common in PCiS vs. ACiS (20% vs. 14%, p < 0.001). Small artery occlusion accounted for 47% of solitary brainstem infarctions. CONCLUSION Ischemic stroke subtypes differ between the two phenotypes. Diabetes and male sex have a stronger association with PCiS than ACiS. Definitive MRI-based PCiS diagnosis aids etiological investigation and contributes additional insights into specific risk factors and mechanisms of injury in PCiS.
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Affiliation(s)
- Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden.
- Department of Neurology and Rehabilitation Medicine, Neurology, Skåne University Hospital, Malmö, Sweden.
- Department of Neurology, Skåne University Hospital, Jan Waldenströms gata 19, 205 02, Malmö, Sweden.
| | - Mattias Drake
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - A K Giese
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - J Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
- Department of Radiology, Neuroradiology, Skåne University Hospital, Lund, Sweden
| | - M D Schirmer
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
- Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - K L Donahue
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - L Cloonan
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - R Irie
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - M J R J Bouts
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - E C McIntosh
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - S J T Mocking
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - A V Dalca
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - R Sridharan
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
| | - H Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - E Giralt-Steinhauer
- Neurovascular Research Group (NEUVAS), Department of Neurology, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - L Holmegaard
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - K Jood
- Department of Clinical Neuroscience, Institute of Neuroscience and Physiology, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
- Department of Neurology, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - J Roquer
- Neurovascular Research Group (NEUVAS), Department of Neurology, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - J W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - P F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - J P Broderick
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - J Jimenez-Conde
- Neurovascular Research Group (NEUVAS), Department of Neurology, IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - C Jern
- Institute of Biomedicine, The Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - B M Kissela
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - D O Kleindorfer
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - R Lemmens
- Department of Neurosciences, Experimental Neurology, KU Leuven-University of Leuven, Louvain, Belgium
- VIB Center for Brain and Disease Research, Louvain, Belgium
- Department of Neurology, University Hospitals Leuven, Louvain, Belgium
| | - J F Meschia
- Department of Neurology, Mayo Clinic, Jacksonville, FL, USA
| | - T Rundek
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - R L Sacco
- Department of Neurology and Evelyn F. McKnight Brain Institute, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - R Schmidt
- Clinical Division of Neurogeriatrics, Department of Neurology, Medical University Graz, Graz, Austria
| | - P Sharma
- Institute of Cardiovascular Research, Royal Holloway University of London (ICR2UL), Egham, UK
- Ashford and St Peter's Hospital, Ashford, UK
| | - A Slowik
- Department of Neurology, Jagiellonian University Medical College, Kraków, Poland
| | - V Thijs
- Stroke Division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
- Department of Neurology, Austin Health, Heidelberg, Australia
| | - D Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - B B Worrall
- Department of Neurology, University of Virginia, Charlottesville, VA, USA
- Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - S J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - B D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Geriatric Research and Education Clinical Center, Veterans Administration Medical Center, Baltimore, MD, USA
| | - J Petersson
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
- Department of Neurology and Rehabilitation Medicine, Neurology, Skåne University Hospital, Malmö, Sweden
| | - J Rosand
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
- Center for Genomic Research, Massachusetts General Hospital, Boston, MA, USA
- Division of Neurocritical Care and Emergency Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - P Golland
- Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, USA
| | - O Wu
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital (MGH), Harvard Medical School, Charlestown, MA, USA
| | - N S Rost
- J. Philip Kistler Stroke Research Center, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - A Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
- Department of Neurology and Rehabilitation Medicine, Neurology, Skåne University Hospital, Lund, Sweden
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Billot B, Robinson E, Dalca AV, Iglesias JE. Partial Volume Segmentation of Brain MRI Scans of Any Resolution and Contrast. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 2020. [DOI: 10.1007/978-3-030-59728-3_18] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Beaulieu-Jones B, Finlayson SG, Chivers C, Chen I, McDermott M, Kandola J, Dalca AV, Beam A, Fiterau M, Naumann T. Trends and Focus of Machine Learning Applications for Health Research. JAMA Netw Open 2019; 2:e1914051. [PMID: 31651969 PMCID: PMC6822089 DOI: 10.1001/jamanetworkopen.2019.14051] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022] Open
Abstract
IMPORTANCE The use of machine learning applications related to health is rapidly increasing and may have the potential to profoundly affect the field of health care. OBJECTIVE To analyze submissions to a popular machine learning for health venue to assess the current state of research, including areas of methodologic and clinical focus, limitations, and underexplored areas. DESIGN, SETTING, AND PARTICIPANTS In this data-driven qualitative analysis, 166 accepted manuscript submissions to the Third Annual Machine Learning for Health workshop at the 32nd Conference on Neural Information Processing Systems on December 8, 2018, were analyzed to understand research focus, progress, and trends. Experts reviewed each submission against a rubric to identify key data points, statistical modeling and analysis of submitting authors was performed, and research topics were quantitatively modeled. Finally, an iterative discussion of topics common in submissions and invited speakers at the workshop was held to identify key trends. MAIN OUTCOMES AND MEASURES Frequency and statistical measures of methods, topics, goals, and author attributes were derived from an expert review of submissions guided by a rubric. RESULTS Of the 166 accepted submissions, 58 (34.9%) had clinician involvement and 83 submissions (50.0%) that focused on clinical practice included clinical collaborators. A total of 97 data sets (58.4%) used in submissions were publicly available or required a standard registration process. Clinical practice was the most common application area (70 manuscripts [42.2%]), with brain and mental health (25 [15.1%]), oncology (21 [12.7%]), and cardiovascular (19 [11.4%]) being the most common specialties. CONCLUSIONS AND RELEVANCE Trends in machine learning for health research indicate the importance of well-annotated, easily accessed data and the benefit from greater clinician involvement in the development of translational applications.
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Affiliation(s)
- Brett Beaulieu-Jones
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
| | | | - Corey Chivers
- Predictive Health Care Group, University of Pennsylvania Health System, Philadelphia
| | - Irene Chen
- MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts
| | - Matthew McDermott
- MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts
| | - Jaz Kandola
- Department of Medicine, Imperial College London, London, United Kingdom
| | - Adrian V. Dalca
- Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts
- MIT Computer Science and Artificial Intelligence Lab, Boston, Massachusetts
| | - Andrew Beam
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
| | - Madalina Fiterau
- College of Information and Computer Sciences, University of Massachusetts, Amherst
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Dalca AV, Balakrishnan G, Guttag J, Sabuncu MR. Unsupervised learning of probabilistic diffeomorphic registration for images and surfaces. Med Image Anal 2019; 57:226-236. [DOI: 10.1016/j.media.2019.07.006] [Citation(s) in RCA: 46] [Impact Index Per Article: 9.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Revised: 06/12/2019] [Accepted: 07/04/2019] [Indexed: 10/26/2022]
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Dalca AV, Yu E, Golland P, Fischl B, Sabuncu MR, Iglesias JE. Unsupervised Deep Learning for Bayesian Brain MRI Segmentation. Med Image Comput Comput Assist Interv 2019; 11766:356-365. [PMID: 32432231 PMCID: PMC7235150 DOI: 10.1007/978-3-030-32248-9_40] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
Abstract
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often computationally expensive. In contrast, there has been a recent surge of approaches that leverage deep learning to implement segmentation tools that are computationally efficient at test time. However, most of these strategies rely on learning from manually annotated images. These supervised deep learning methods are therefore sensitive to the intensity profiles in the training dataset. To develop a deep learning-based segmentation model for a new image dataset (e.g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches. In this paper, we propose an alternative strategy that combines a conventional probabilistic atlas-based segmentation with deep learning, enabling one to train a segmentation model for new MRI scans without the need for any manually segmented images. Our experiments include thousands of brain MRI scans and demonstrate that the proposed method achieves good accuracy for a brain MRI segmentation task for different MRI contrasts, requiring only approximately 15 seconds at test time on a GPU.
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Affiliation(s)
- Adrian V Dalca
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology
| | - Evan Yu
- Meinig School of Biomedical Engineering, Cornell University
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
| | - Mert R Sabuncu
- Meinig School of Biomedical Engineering, Cornell University
- School of Electrical and Computer Engineering, Cornell University
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School
- Computer Science and Artificial Intelligence Laboratory (CSAIL), Massachusetts Institute of Technology
- Centre for Medical Image Computing (CMIC), University College London
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46
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Schirmer MD, Dalca AV, Sridharan R, Giese AK, Donahue KL, Nardin MJ, Mocking SJT, McIntosh EC, Frid P, Wasselius J, Cole JW, Holmegaard L, Jern C, Jimenez-Conde J, Lemmens R, Lindgren AG, Meschia JF, Roquer J, Rundek T, Sacco RL, Schmidt R, Sharma P, Slowik A, Thijs V, Woo D, Vagal A, Xu H, Kittner SJ, McArdle PF, Mitchell BD, Rosand J, Worrall BB, Wu O, Golland P, Rost NS. White matter hyperintensity quantification in large-scale clinical acute ischemic stroke cohorts - The MRI-GENIE study. Neuroimage Clin 2019; 23:101884. [PMID: 31200151 PMCID: PMC6562316 DOI: 10.1016/j.nicl.2019.101884] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2019] [Revised: 05/02/2019] [Accepted: 05/25/2019] [Indexed: 11/26/2022]
Abstract
White matter hyperintensity (WMH) burden is a critically important cerebrovascular phenotype linked to prediction of diagnosis and prognosis of diseases, such as acute ischemic stroke (AIS). However, current approaches to its quantification on clinical MRI often rely on time intensive manual delineation of the disease on T2 fluid attenuated inverse recovery (FLAIR), which hinders high-throughput analyses such as genetic discovery. In this work, we present a fully automated pipeline for quantification of WMH in clinical large-scale studies of AIS. The pipeline incorporates automated brain extraction, intensity normalization and WMH segmentation using spatial priors. We first propose a brain extraction algorithm based on a fully convolutional deep learning architecture, specifically designed for clinical FLAIR images. We demonstrate that our method for brain extraction outperforms two commonly used and publicly available methods on clinical quality images in a set of 144 subject scans across 12 acquisition centers, based on dice coefficient (median 0.95; inter-quartile range 0.94–0.95; p < 0.01) and Pearson correlation of total brain volume (r = 0.90). Subsequently, we apply it to the large-scale clinical multi-site MRI-GENIE study (N = 2783) and identify a decrease in total brain volume of −2.4 cc/year. Additionally, we show that the resulting total brain volumes can successfully be used for quality control of image preprocessing. Finally, we obtain WMH volumes by building on an existing automatic WMH segmentation algorithm that delineates and distinguishes between different cerebrovascular pathologies. The learning method mimics expert knowledge of the spatial distribution of the WMH burden using a convolutional auto-encoder. This enables successful computation of WMH volumes of 2533 clinical AIS patients. We utilize these results to demonstrate the increase of WMH burden with age (0.950 cc/year) and show that single site estimates can be biased by the number of subjects recruited. Fully automated high-throughput white matter hyperintensity segmentation pipeline. Methodology designed for and applied to international clinical multi-site data. Calculation of disease burden in 2533 acute ischemic stroke patients. Total brain volume change with age (−2.4 cc/year) used in automated quality control. Increase of white matter hyperintensity burden of 0.95 cc/year.
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Affiliation(s)
- Markus D Schirmer
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Lab, MIT, USA; Department of Population Health Sciences, German Centre for Neurodegenerative Diseases (DZNE), Germany.
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, MIT, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | | | - Anne-Katrin Giese
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kathleen L Donahue
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marco J Nardin
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Steven J T Mocking
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Elissa C McIntosh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Petrea Frid
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden
| | - Johan Wasselius
- Department of Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden; Department of Radiology, Neuroradiology, Skåne University Hospital, Malmö, Sweden
| | - John W Cole
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Lukas Holmegaard
- Institute of Neuroscience and Physiology, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Christina Jern
- Institute of Biomedicine, the Sahlgrenska Academy at University of Gothenburg, Gothenburg, Sweden
| | - Jordi Jimenez-Conde
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Robin Lemmens
- Department of Neurosciences, Experimental Neurology and Leuven Research Institute for Neuroscience and Disease (LIND), KU Leuven - University of Leuven, Leuven, Belgium; VIB, Vesalius Research Center, Laboratory of Neurobiology, Department of Neurology, University Hospitals Leuven, Leuven, Belgium
| | - Arne G Lindgren
- Department of Clinical Sciences Lund, Neurology, Lund University, Lund, Sweden; Department of Neurology and Rehabilitation Medicine, Skåne University Hospital, Lund, Sweden
| | | | - Jaume Roquer
- Department of Neurology, Neurovascular Research Group (NEUVAS), IMIM-Hospital del Mar (Institut Hospital del Mar d'Investigacions Mèdiques), Universitat Autonoma de Barcelona, Barcelona, Spain
| | - Tatjana Rundek
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Ralph L Sacco
- Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA
| | - Reinhold Schmidt
- Department of Neurology, Clinical Division of Neurogeriatrics, Medical University Graz, Graz, Austria
| | - Pankaj Sharma
- Institute of Cardiovascular Research, St Peter's and Ashford Hospitals, Royal Holloway University of London (ICR2UL), Egham, UK
| | - Agnieszka Slowik
- Department of Neurology, Jagiellonian University Medical College, Krakow, Poland
| | - Vincent Thijs
- Stroke Division, Australia and Department of Neurology, Austin Health, Florey Institute of Neuroscience and Mental Health, Heidelberg, Australia
| | - Daniel Woo
- Department of Neurology and Rehabilitation Medicine, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Achala Vagal
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Huichun Xu
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Steven J Kittner
- Department of Neurology, University of Maryland School of Medicine and Veterans Affairs Maryland Health Care System, Baltimore, MD, USA
| | - Patrick F McArdle
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Braxton D Mitchell
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Jonathan Rosand
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA, USA
| | - Bradford B Worrall
- Departments of Neurology and Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Ona Wu
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Lab, MIT, USA
| | - Natalia S Rost
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Balakrishnan G, Zhao A, Sabuncu MR, Guttag J, Dalca AV. VoxelMorph: A Learning Framework for Deformable Medical Image Registration. IEEE Trans Med Imaging 2019; 38:1788-1800. [PMID: 30716034 DOI: 10.1109/tmi.2019.2897538] [Citation(s) in RCA: 466] [Impact Index Per Article: 93.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/07/2023]
Abstract
We present VoxelMorph, a fast learning-based framework for deformable, pairwise medical image registration. Traditional registration methods optimize an objective function for each pair of images, which can be time-consuming for large datasets or rich deformation models. In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images. We parameterize the function via a convolutional neural network (CNN), and optimize the parameters of the neural network on a set of images. Given a new pair of scans, VoxelMorph rapidly computes a deformation field by directly evaluating the function. In this work, we explore two different training strategies. In the first (unsupervised) setting, we train the model to maximize standard image matching objective functions that are based on the image intensities. In the second setting, we leverage auxiliary segmentations available in the training data. We demonstrate that the unsupervised model's accuracy is comparable to state-of-the-art methods, while operating orders of magnitude faster. We also show that VoxelMorph trained with auxiliary data improves registration accuracy at test time, and evaluate the effect of training set size on registration. Our method promises to speed up medical image analysis and processing pipelines, while facilitating novel directions in learning-based registration and its applications. Our code is freely available at https://github.com/voxelmorph/voxelmorph.
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48
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Bahadir CD, Dalca AV, Sabuncu MR. Learning-Based Optimization of the Under-Sampling Pattern in MRI. Lecture Notes in Computer Science 2019. [DOI: 10.1007/978-3-030-20351-1_61] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
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Pace DF, Dalca AV, Brosch T, Geva T, Powell AJ, Weese J, Moghari MH, Golland P. Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease. Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018) 2018; 11045:334-342. [PMID: 31172133 DOI: 10.1007/978-3-030-00889-5_38] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the intermediate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incomplete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Compared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.
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Affiliation(s)
- Danielle F Pace
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA
| | - Adrian V Dalca
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, HMS, Boston, USA
- School of Electrical and Computer Engineering, Cornell University, Ithaca, USA
| | - Tom Brosch
- Philips Research Laboratories, Hamburg, Germany
| | - Tal Geva
- Department of Cardiology, Boston Children's Hospital, Boston, USA
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | - Andrew J Powell
- Department of Cardiology, Boston Children's Hospital, Boston, USA
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | | | - Mehdi H Moghari
- Department of Cardiology, Boston Children's Hospital, Boston, USA
- Department of Pediatrics, Harvard Medical School, Boston, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, USA
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Dalca AV, Bouman KL, Freeman WT, Rost NS, Sabuncu MR, Golland P. Medical Image Imputation from Image Collections. IEEE Trans Med Imaging 2018; 38:10.1109/TMI.2018.2866692. [PMID: 30136936 PMCID: PMC6393212 DOI: 10.1109/tmi.2018.2866692] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/30/2023]
Abstract
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago.
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Affiliation(s)
- Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, MIT (main contact: ) and also Martinos Center for Biomedical Imaging, Massachusetts General Hospital, HMS
| | | | | | - Natalia S. Rost
- Department of Neurology, Massachusetts General Hospital, HMS
| | - Mert R. Sabuncu
- School of Electrical and Computer Engineering, and Meinig School of Biomedical Engineering, Cornell University
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