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Tayebi Arasteh S, Romanowicz J, Pace DF, Golland P, Powell AJ, Maier AK, Truhn D, Brosch T, Weese J, Lotfinia M, van der Geest RJ, Moghari MH. Automated segmentation of 3D cine cardiovascular magnetic resonance imaging. Front Cardiovasc Med 2023; 10:1167500. [PMID: 37904806 PMCID: PMC10613522 DOI: 10.3389/fcvm.2023.1167500] [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: 02/16/2023] [Accepted: 09/18/2023] [Indexed: 11/01/2023] Open
Abstract
Introduction As the life expectancy of children with congenital heart disease (CHD) is rapidly increasing and the adult population with CHD is growing, there is an unmet need to improve clinical workflow and efficiency of analysis. Cardiovascular magnetic resonance (CMR) is a noninvasive imaging modality for monitoring patients with CHD. CMR exam is based on multiple breath-hold 2-dimensional (2D) cine acquisitions that should be precisely prescribed and is expert and institution dependent. Moreover, 2D cine images have relatively thick slices, which does not allow for isotropic delineation of ventricular structures. Thus, development of an isotropic 3D cine acquisition and automatic segmentation method is worthwhile to make CMR workflow straightforward and efficient, as the present work aims to establish. Methods Ninety-nine patients with many types of CHD were imaged using a non-angulated 3D cine CMR sequence covering the whole-heart and great vessels. Automatic supervised and semi-supervised deep-learning-based methods were developed for whole-heart segmentation of 3D cine images to separately delineate the cardiac structures, including both atria, both ventricles, aorta, pulmonary arteries, and superior and inferior vena cavae. The segmentation results derived from the two methods were compared with the manual segmentation in terms of Dice score, a degree of overlap agreement, and atrial and ventricular volume measurements. Results The semi-supervised method resulted in a better overlap agreement with the manual segmentation than the supervised method for all 8 structures (Dice score 83.23 ± 16.76% vs. 77.98 ± 19.64%; P-value ≤0.001). The mean difference error in atrial and ventricular volumetric measurements between manual segmentation and semi-supervised method was lower (bias ≤ 5.2 ml) than the supervised method (bias ≤ 10.1 ml). Discussion The proposed semi-supervised method is capable of cardiac segmentation and chamber volume quantification in a CHD population with wide anatomical variability. It accurately delineates the heart chambers and great vessels and can be used to accurately calculate ventricular and atrial volumes throughout the cardiac cycle. Such a segmentation method can reduce inter- and intra- observer variability and make CMR exams more standardized and efficient.
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Affiliation(s)
- Soroosh Tayebi Arasteh
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Jennifer Romanowicz
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
- Department of Cardiology, Children’s Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States
| | - Danielle F. Pace
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
- Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Polina Golland
- Computer Science & Artificial Intelligence Lab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Andrew J. Powell
- Department of Cardiology, Boston Children’s Hospital, and Department of Pediatrics, Harvard Medical School, Boston, MA, United States
| | - Andreas K. Maier
- Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - Daniel Truhn
- Department of Diagnostic and Interventional Radiology, University Hospital RWTH Aachen, Aachen, Germany
| | - Tom Brosch
- Philips Research Laboratories, Hamburg, Germany
| | | | - Mahshad Lotfinia
- Institute of Heat and Mass Transfer, RWTH Aachen University, Aachen, Germany
| | | | - Mehdi H. Moghari
- Department of Radiology, Children’s Hospital Colorado, and School of Medicine, University of Colorado, Aurora, CO, United States
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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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Bayat A, Pace DF, Sekuboyina A, Payer C, Stern D, Urschler M, Kirschke JS, Menze BH. Anatomy-Aware Inference of the 3D Standing Spine Posture from 2D Radiographs. Tomography 2022; 8:479-496. [PMID: 35202204 PMCID: PMC8879677 DOI: 10.3390/tomography8010039] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [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: 12/06/2021] [Revised: 01/30/2022] [Accepted: 02/03/2022] [Indexed: 11/21/2022] Open
Abstract
An important factor for the development of spinal degeneration, pain and the outcome of spinal surgery is known to be the balance of the spine. It must be analyzed in an upright, standing position to ensure physiological loading conditions and visualize load-dependent deformations. Despite the complex 3D shape of the spine, this analysis is currently performed using 2D radiographs, as all frequently used 3D imaging techniques require the patient to be scanned in a prone position. To overcome this limitation, we propose a deep neural network to reconstruct the 3D spinal pose in an upright standing position, loaded naturally. Specifically, we propose a novel neural network architecture, which takes orthogonal 2D radiographs and infers the spine’s 3D posture using vertebral shape priors. In this work, we define vertebral shape priors using an atlas and a spine shape prior, incorporating both into our proposed network architecture. We validate our architecture on digitally reconstructed radiographs, achieving a 3D reconstruction Dice of 0.95, indicating an almost perfect 2D-to-3D domain translation. Validating the reconstruction accuracy of a 3D standing spine on real data is infeasible due to the lack of a valid ground truth. Hence, we design a novel experiment for this purpose, using an orientation invariant distance metric, to evaluate our model’s ability to synthesize full-3D, upright, and patient-specific spine models. We compare the synthesized spine shapes from clinical upright standing radiographs to the same patient’s 3D spinal posture in the prone position from CT.
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Affiliation(s)
- Amirhossein Bayat
- Department of Computer Science, Technical University of Munich, 85748 Garching, Germany; (A.S.); (B.H.M.)
- Department of Neuroradiology, Klinikum rech der Isar, 81675 Munich, Germany;
- Correspondence:
| | - Danielle F. Pace
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;
- A.A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Anjany Sekuboyina
- Department of Computer Science, Technical University of Munich, 85748 Garching, Germany; (A.S.); (B.H.M.)
- Department of Neuroradiology, Klinikum rech der Isar, 81675 Munich, Germany;
- Department of Quantitative Biomedicine, University of Zurich, 8006 Zurich, Switzerland
| | - Christian Payer
- Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria; (C.P.); (D.S.)
| | - Darko Stern
- Institute of Computer Graphics and Vision, Graz University of Technology, 8010 Graz, Austria; (C.P.); (D.S.)
| | - Martin Urschler
- School of Computer Science, University of Auckland, Auckland 1010, New Zealand;
| | - Jan S. Kirschke
- Department of Neuroradiology, Klinikum rech der Isar, 81675 Munich, Germany;
| | - Bjoern H. Menze
- Department of Computer Science, Technical University of Munich, 85748 Garching, Germany; (A.S.); (B.H.M.)
- Department of Quantitative Biomedicine, University of Zurich, 8006 Zurich, Switzerland
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Herz C, Pace DF, Nam HH, Lasso A, Dinh P, Flynn M, Cianciulli A, Golland P, Jolley MA. Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning. Front Cardiovasc Med 2021; 8:735587. [PMID: 34957233 PMCID: PMC8696083 DOI: 10.3389/fcvm.2021.735587] [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] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Accepted: 11/16/2021] [Indexed: 11/13/2022] Open
Abstract
Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81-0.88] and MBD of 0.35 [0.23-0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73-0.81] and MBD of 0.6 [0.44-0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3-0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.
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Affiliation(s)
- Christian Herz
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
| | - Danielle F. Pace
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Hannah H. Nam
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, ON, Canada
| | - Patrick Dinh
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
| | - Maura Flynn
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
| | - Alana Cianciulli
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Matthew A. Jolley
- Children's Hospital of Philadelphia, Department of Anesthesia and Critical Care Medicine, Philadelphia, PA, United States
- Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
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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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Pace DF, Dalca AV, Geva T, Powell AJ, Moghari MH, Golland P. Interactive Whole-Heart Segmentation in Congenital Heart Disease. Med Image Comput Comput Assist Interv 2015; 9351:80-88. [PMID: 26889498 PMCID: PMC4753059 DOI: 10.1007/978-3-319-24574-4_10] [Citation(s) in RCA: 52] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
We present an interactive algorithm to segment the heart chambers and epicardial surfaces, including the great vessel walls, in pediatric cardiac MRI of congenital heart disease. Accurate whole-heart segmentation is necessary to create patient-specific 3D heart models for surgical planning in the presence of complex heart defects. Anatomical variability due to congenital defects precludes fully automatic atlas-based segmentation. Our interactive segmentation method exploits expert segmentations of a small set of short-axis slice regions to automatically delineate the remaining volume using patch-based segmentation. We also investigate the potential of active learning to automatically solicit user input in areas where segmentation error is likely to be high. Validation is performed on four subjects with double outlet right ventricle, a severe congenital heart defect. We show that strategies asking the user to manually segment regions of interest within short-axis slices yield higher accuracy with less user input than those querying entire short-axis slices.
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Affiliation(s)
- Danielle F. Pace
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA
| | - Adrian V. Dalca
- Computer Science and Artificial Intelligence Lab, MIT, Cambridge, MA, USA
| | - 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 Lab, MIT, Cambridge, MA, USA
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Pace DF, Aylward SR, Niethammer M. A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE Trans Med Imaging 2013; 32:2114-26. [PMID: 23899632 PMCID: PMC4112204 DOI: 10.1109/tmi.2013.2274777] [Citation(s) in RCA: 28] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/05/2023]
Abstract
We propose a deformable image registration algorithm that uses anisotropic smoothing for regularization to find correspondences between images of sliding organs. In particular, we apply the method for respiratory motion estimation in longitudinal thoracic and abdominal computed tomography scans. The algorithm uses locally adaptive diffusion tensors to determine the direction and magnitude with which to smooth the components of the displacement field that are normal and tangential to an expected sliding boundary. Validation was performed using synthetic, phantom, and 14 clinical datasets, including the publicly available DIR-Lab dataset. We show that motion discontinuities caused by sliding can be effectively recovered, unlike conventional regularizations that enforce globally smooth motion. In the clinical datasets, target registration error showed improved accuracy for lung landmarks compared to the diffusive regularization. We also present a generalization of our algorithm to other sliding geometries, including sliding tubes (e.g., needles sliding through tissue, or contrast agent flowing through a vessel). Potential clinical applications of this method include longitudinal change detection and radiotherapy for lung or abdominal tumours, especially those near the chest or abdominal wall.
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Irimia A, Wang B, Aylward SR, Prastawa MW, Pace DF, Gerig G, Hovda DA, Kikinis R, Vespa PM, Van Horn JD. Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction. Neuroimage Clin 2012; 1:1-17. [PMID: 24179732 PMCID: PMC3757727 DOI: 10.1016/j.nicl.2012.08.002] [Citation(s) in RCA: 74] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/19/2012] [Revised: 08/14/2012] [Accepted: 08/15/2012] [Indexed: 11/01/2022]
Abstract
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome.
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Key Words
- 3D, three-dimensional
- AAL, Automatic Anatomical Labeling
- ADC, apparent diffusion coefficient
- ANTS, Advanced Normalization ToolS
- BOLD, blood oxygen level dependent
- CC, corpus callosum
- CT, computed tomography
- DAI, diffuse axonal injury
- DSI, diffusion spectrum imaging
- DTI, diffusion tensor imaging
- DWI, diffusion weighted imaging
- Diffusion tensor
- FA, fractional anisotropy
- FLAIR, Fluid Attenuated Inversion Recovery
- FSE, Functional Status Examination
- GCS, Glasgow Coma Score
- GM, gray matter
- GOS, Glasgow Outcome Score
- GRE, Gradient Recalled Echo
- HARDI, high-angular-resolution diffusion imaging
- IBA, Individual Brain Atlas
- LDA, linear discriminant analysis
- MRI, magnetic resonance imaging
- MRI/fMRI
- NINDS, National Institute of Neurological Disorders and Stroke
- Neuroimaging
- Outcome measures
- PCA, principal component analysis
- PROMO, PROspective MOtion Correction
- SPM, Statistical Parametric Mapping
- SWI, Susceptibility Weighted Imaging
- TBI, traumatic brain injury
- TBSS, tract-based spatial statistics
- Trauma
- WM, white matter
- fMRI, functional magnetic resonance imaging
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Affiliation(s)
- Andrei Irimia
- Laboratory of Neuro Imaging, Department of Neurology, University of California, Los Angeles, CA 90095, USA
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Pace DF, Enquobahrie A, Yang H, Aylward SR, Niethammer M. Deformable Image Registration of Sliding Organs Using Anisotropic Diffusive Regularization. Proc IEEE Int Symp Biomed Imaging 2011:407-413. [PMID: 21785755 DOI: 10.1109/isbi.2011.5872434] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Traditional deformable image registration imposes a uniform smoothness constraint on the deformation field. This is not appropriate when registering images visualizing organs that slide relative to each other, and therefore leads to registration inaccuracies. In this paper, we present a deformation field regularization term that is based on anisotropic diffusion and accommodates the deformation field discontinuities that are expected when considering sliding motion. The registration algorithm was assessed first using artificial images of geometric objects. In a second validation, monomodal chest images depicting both respiratory and cardiac motion were generated using an anatomically-realistic software phantom and then registered. Registration accuracy was assessed based on the distances between corresponding segmented organ surfaces. Compared to an established diffusive regularization approach, the anisotropic diffusive regularization gave deformation fields that represented more plausible image correspondences, while giving rise to similar transformed moving images and comparable registration accuracy.
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Affiliation(s)
- Marc Niethammer
- University of North Carolina (UNC), Chapel Hill NC 27599-3175, USA
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