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Volniansky A, Lefebvre TL, Kulbay M, Fan B, Aslan E, Vu KN, Montagnon E, Nguyen BN, Sebastiani G, Giard JM, Sylvestre MP, Gilbert G, Cloutier G, Tang A. Inter-visit and inter-reader reproducibility of multi-parametric diffusion-weighted MR imaging in longitudinally imaged patients with metabolic dysfunction-associated fatty liver disease and healthy volunteers. Magn Reson Imaging 2024; 113:110223. [PMID: 39181478 DOI: 10.1016/j.mri.2024.110223] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2024] [Revised: 07/31/2024] [Accepted: 08/21/2024] [Indexed: 08/27/2024]
Abstract
BACKGROUND Despite the widespread use of diffusion-weighted imaging (DWI) in metabolic dysfunction-associated fatty liver disease (MAFLD), MRI acquisition and quantification techniques vary in the literature suggesting the need for established and reproducible protocols. The goal of this study was to assess inter-visit and inter-reader reproducibility of DWI- and IVIM-derived parameters in patients with MAFLD and healthy volunteers using extensive sampling of the "fast" compartment, non-rigid registration, and exclusion voxels with poor fit quality. METHODS From June 2019 to April 2023, 31 subjects (20 patients with biopsy-proven MAFLD and 11 healthy volunteers) were included in this IRB-approved study. Subjects underwent MRI examinations twice within 40 days. 3.0 T DWI was acquired using a respiratory-triggered spin-echo diffusion-weighted echo-planar imaging sequence (b-values of 0, 10, 20, 30, 40, 50, 100, 200, 400, 800 s/mm2). DWI series were co-registered prior to voxel-wise non-linear regression of the IVIM model and voxels with poor fit quality were excluded (normalized root mean squared error ≥ 0.05). IVIM parameters (perfusion fraction, f; diffusion coefficient, D; and pseudo-diffusion coefficient, D*), and apparent diffusion coefficients (ADC) were computed from manual segmentation of the right liver lobe performed by two analysts on two MRI examinations. RESULTS All results are reported for f, D, D*, and ADC respectively. For inter-reader agreement on the first visit, ICC were of 0.985, 0.994, 0.986, and 0.993 respectively. For intra-reader agreement of analyst 1 assessed on both imaging examinations, ICC between visits were of 0.805, 0.759, 0.511, and 0.850 respectively. For inter-reader agreement on the first visit, mean bias and 95 % limits of agreement were (0.00 ± 0.03), (-0.01 ± 0.03) × 10-3 mm2/s, (0.70 ± 10.40) × 10-3 mm2/s, and (-0.02 ± 0.04) × 10-3 mm2/s respectively. For intra-reader agreement of analyst 1, mean bias and 95 % limits of agreement were (0.01 ± 0.09) × 10-3 mm2/s, (-0.01 ± 0.21) × 10-3 mm2/s, (-13.37 ± 56.19) × 10-3 mm2/s, and (-0.01 ± 0.16) × 10-3 mm2/s respectively. Except for parameter D* that was associated with between-subjects parameter variability (P = 0.009), there was no significant variability between subjects, examinations, or readers. CONCLUSION With our approach, IVIM parameters f, D, D*, and ADC provided excellent inter-reader agreement and good to very good inter-visit or intra-reader agreement, thus showing the reproducibility of IVIM-DWI of the liver in MAFLD patients and volunteers.
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Affiliation(s)
- Anton Volniansky
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
| | - Thierry L Lefebvre
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Department of Physics, University of Cambridge, Cambridge, United Kingdom; Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom.
| | - Merve Kulbay
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Department of Ophthalmology & Visual Sciences, McGill University, Montréal, Canada.
| | - Boyan Fan
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
| | - Emre Aslan
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
| | - Kim-Nhien Vu
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada.
| | - Emmanuel Montagnon
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada
| | - Bich Ngoc Nguyen
- Service of Pathology, Centre Hospitalier de l'Université de Montréal (CHUM), Montréal, Canada.
| | - Giada Sebastiani
- Department of Medicine, Division of Gastroenterology and Hepatology, McGill University Health Centre (MUHC), Montréal, Canada.
| | - Jeanne-Marie Giard
- Department of Medicine, Division of Hepatology and Liver Transplantation, Université de Montréal, Montréal, Canada
| | - Marie-Pierre Sylvestre
- Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Department of Social and Preventive Medicine, École de santé publique de l'Université de Montréal (ESPUM), Montréal, Canada.
| | - Guillaume Gilbert
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; MR Clinical Science, Philips Healthcare Canada, Mississauga, Canada.
| | - Guy Cloutier
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada; Laboratory of Biorheology and Medical Ultrasonics (LBUM), Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada.
| | - An Tang
- Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada; Centre de recherche du Centre hospitalier de l'Université de Montréal (CRCHUM), Montréal, Canada; Institute of Biomedical Engineering, Université de Montréal, Montréal, Canada.
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Voicu IP, Dotta F, Napolitano A, Caulo M, Piccirilli E, D’Orazio C, Carai A, Miele E, Vinci M, Rossi S, Cacchione A, Vennarini S, Del Baldo G, Mastronuzzi A, Tomà P, Colafati GS. Machine Learning Analysis in Diffusion Kurtosis Imaging for Discriminating Pediatric Posterior Fossa Tumors: A Repeatability and Accuracy Pilot Study. Cancers (Basel) 2024; 16:2578. [PMID: 39061217 PMCID: PMC11274924 DOI: 10.3390/cancers16142578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2024] [Revised: 07/02/2024] [Accepted: 07/05/2024] [Indexed: 07/28/2024] Open
Abstract
Background and purpose: Differentiating pediatric posterior fossa (PF) tumors such as medulloblastoma (MB), ependymoma (EP), and pilocytic astrocytoma (PA) remains relevant, because of important treatment and prognostic implications. Diffusion kurtosis imaging (DKI) has not yet been investigated for discrimination of pediatric PF tumors. Estimating diffusion values from whole-tumor-based (VOI) segmentations may improve diffusion measurement repeatability compared to conventional region-of-interest (ROI) approaches. Our purpose was to compare repeatability between ROI and VOI DKI-derived diffusion measurements and assess DKI accuracy in discriminating among pediatric PF tumors. Materials and methods: We retrospectively analyzed 34 children (M, F, mean age 7.48 years) with PF tumors who underwent preoperative examination on a 3 Tesla magnet, including DKI. For each patient, two neuroradiologists independently segmented the whole solid tumor, the ROI of the area of maximum tumor diameter, and a small 5 mm ROI. The automated analysis pipeline included inter-observer variability, statistical, and machine learning (ML) analyses. We evaluated inter-observer variability with coefficient of variation (COV) and Bland-Altman plots. We estimated DKI metrics accuracy in discriminating among tumor histology with MANOVA analysis. In order to account for class imbalances, we applied SMOTE to balance the dataset. Finally, we performed a Random Forest (RF) machine learning classification analysis based on all DKI metrics from the SMOTE dataset by partitioning 70/30 the training and testing cohort. Results: Tumor histology included medulloblastoma (15), pilocytic astrocytoma (14), and ependymoma (5). VOI-based measurements presented lower variability than ROI-based measurements across all DKI metrics and were used for the analysis. DKI-derived metrics could accurately discriminate between tumor subtypes (Pillai's trace: p < 0.001). SMOTE generated 11 synthetic observations (10 EP and 1 PA), resulting in a balanced dataset with 45 instances (34 original and 11 synthetic). ML analysis yielded an accuracy of 0.928, which correctly predicted all but one lesion in the testing set. Conclusions: VOI-based measurements presented improved repeatability compared to ROI-based measurements across all diffusion metrics. An ML classification algorithm resulted accurate in discriminating PF tumors on a SMOTE-generated dataset. ML techniques based on DKI-derived metrics are useful for the discrimination of pediatric PF tumors.
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Affiliation(s)
- Ioan Paul Voicu
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
| | - Francesco Dotta
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
- Department of Innovative Technologies in Medicine and Dentistry, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
| | - Antonio Napolitano
- Medical Physics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Massimo Caulo
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy;
| | - Eleonora Piccirilli
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
- Department of Neuroscience, Imaging and Clinical Sciences, University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy;
| | - Claudia D’Orazio
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
| | - Andrea Carai
- Neurosurgery Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Evelina Miele
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Maria Vinci
- Paediatric Cancer Genetics and Epigenetics Research Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Sabrina Rossi
- Pathology Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Antonella Cacchione
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Sabina Vennarini
- Pediatric Radiotherapy Unit, IRCCS Fondazione Istituto Nazionale Tumori, 20133 Milano, Italy;
| | - Giada Del Baldo
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Angela Mastronuzzi
- Onco-Hematology, Cell Therapy, Gene Therapies and Hemopoietic Transplant, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (E.M.); (A.C.); (G.D.B.); (A.M.)
| | - Paolo Tomà
- Radiology and Bioimaging Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy;
| | - Giovanna Stefania Colafati
- Oncological Neuroradiology and Advanced Diagnostics Unit, Bambino Gesù Children’s Hospital, IRCCS, 00165 Rome, Italy; (I.P.V.); (F.D.); (E.P.); (C.D.)
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Munsch F, Planes D, Fukutomi H, Marnat G, Courret T, Micard E, Chen B, Seners P, Dubos J, Planche V, Coupé P, Dousset V, Lapergue B, Olivot JM, Sibon I, Thiebaut De Schotten M, Tourdias T. Dynamic Evolution of Infarct Volumes at MRI in Ischemic Stroke Due to Large Vessel Occlusion. Neurology 2024; 102:e209427. [PMID: 38815232 DOI: 10.1212/wnl.0000000000209427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/01/2024] Open
Abstract
BACKGROUND AND OBJECTIVES The typical infarct volume trajectories in stroke patients, categorized as slow or fast progressors, remain largely unknown. This study aimed to reveal the characteristic spatiotemporal evolutions of infarct volumes caused by large vessel occlusion (LVO) and show that such growth charts help anticipate clinical outcomes. METHODS We conducted a secondary analysis from prospectively collected databases (FRAME, 2017-2019; ETIS, 2015-2022). We selected acute MRI data from anterior LVO stroke patients with witnessed onset, which were divided into training and independent validation datasets. In the training dataset, using Gaussian mixture analysis, we classified the patients into 3 growth groups based on their rate of infarct growth (diffusion volume/time-to-imaging). Subsequently, we extrapolated pseudo-longitudinal models of infarct growth for each group and generated sequential frequency maps to highlight the spatial distribution of infarct growth. We used these charts to attribute a growth group to the independent patients from the validation dataset. We compared their 3-month modified Rankin scale (mRS) with the predicted values based on a multivariable regression model from the training dataset that used growth group as an independent variable. RESULTS We included 804 patients (median age 73.0 years [interquartile range 61.2-82.0 years]; 409 men). The training dataset revealed nonsupervised clustering into 11% (74/703) slow, 62% (437/703) intermediate, and 27% (192/703) fast progressors. Infarct volume evolutions were best fitted with a linear (r = 0.809; p < 0.001), cubic (r = 0.471; p < 0.001), and power (r = 0.63; p < 0.001) function for the slow, intermediate, and fast progressors, respectively. Notably, the deep nuclei and insular cortex were rapidly affected in the intermediate and fast groups with further cortical involvement in the fast group. The variable growth group significantly predicted the 3-month mRS (multivariate odds ratio 0.51; 95% CI 0.37-0.72, p < 0.0001) in the training dataset, yielding a mean area under the receiver operating characteristic curve of 0.78 (95% CI 0.66-0.88) in the independent validation dataset. DISCUSSION We revealed spatiotemporal archetype dynamic evolutions following LVO stroke according to 3 growth phenotypes called slow, intermediate, and fast progressors, providing insight into anticipating clinical outcome. We expect this could help in designing neuroprotective trials aiming at modulating infarct growth before EVT.
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Affiliation(s)
- Fanny Munsch
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - David Planes
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Hikaru Fukutomi
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Gaultier Marnat
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Thomas Courret
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Emilien Micard
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Bailiang Chen
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Pierre Seners
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Johanna Dubos
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Vincent Planche
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Pierrick Coupé
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Vincent Dousset
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Bertrand Lapergue
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Jean Marc Olivot
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Igor Sibon
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Michel Thiebaut De Schotten
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
| | - Thomas Tourdias
- From the Institut de Bio-imagerie IBIO (F.M., J.D., V.D., T.T.), University Bordeaux; Neuroimagerie Diagnostique et Thérapeutique (D.P., G.M., T.C., V.D., T.T.), CHU de Bordeaux, France; Kansai Electric Power Hospital (H.F.), Osaka, Japan; Inserm CIC-IT U1433 (E.M., B.C.), CHRU Nancy; Institut de Psychiatrie et Neurosciences de Paris (IPNP) (P.S.), INSERM U1266; Département de Neurologie (P.S.), Hopital Fondation Rothschild, Paris; Institut des Maladies Neurodégénératives (V.P.), CNRS, UMR 5293, Bordeaux INP (P.C.), LABRI, CNRS, UMR5800, and Neurocentre Magendie (V.D., T.T.), INSERM U1215, Univ. Bordeaux; Service de Neurologie et Unité de Neuro Vasculaire (B.L.), Hôpital FOCH, Suresnes; Unité Neurovasculaire (J.M.O.), CHU de Toulouse; Unité Neurovasculaire (I.S.), CHU de Bordeaux; CNRS (M.T.D.S.), UMR-5293, Univ. Bordeaux; and Brain Connectivity and Behaviour Laboratory (M.T.D.S.), Paris, France
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5
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Bano W, Pulli E, Cantonas L, Sorsa A, Hämäläinen J, Karlsson H, Karlsson L, Saukko E, Sainio T, Peuna A, Korja R, Aro M, Leppänen PH, Tuulari JJ, Merisaari H. Implementing ABCD study Ⓡ MRI sequences for multi-site cohort studies: Practical guide to necessary steps, preprocessing methods, and challenges. MethodsX 2024; 12:102789. [PMID: 38966716 PMCID: PMC11223117 DOI: 10.1016/j.mex.2024.102789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2024] [Accepted: 05/31/2024] [Indexed: 07/06/2024] Open
Abstract
Large multi-site studies that combine magnetic resonance imaging (MRI) data across research sites present exceptional opportunities to advance neuroscience research. However, scanner or site variability and non-standardised image acquisition protocols, data processing and analysis pipelines can adversely affect the reliability and repeatability of MRI derived brain measures. We implemented a standardised MRI protocol based on that used in the Adolescent Brain Cognition Development (ABCD)Ⓡ study in two sites, and across four MRI scanners. Twice repeated measurements of a single healthy volunteer were obtained in two sites and in four 3T MRI scanners (vendors: Siemens, Philips, and GE). Imaging data included anatomical scans (T1 weighted, T2 weighted), diffusion weighted imaging (DWI) and resting state functional MRI (rs-fMRI). Standardised containerized pipelines were utilised to pre-process the data and different image quality metrics and test-retest variability of different brain metrics were evaluated. The implementation of the MRI protocols was possible with minor adjustments in acquisition (e.g. repetition time (TR), higher b-values) and exporting (DICOM formats) of images due to different technical performance of the scanners. This study provides practical insights into the implementation of standardised sequences and data processing for multisite studies, showcase the benefits of containerised preprocessing tools, and highlights the need for careful optimisation of multisite image acquisition.
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Affiliation(s)
- Wajiha Bano
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
| | - Elmo Pulli
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
| | - Lucia Cantonas
- Department of Psychology and Education, University of Jyväskylä, Finland
| | - Aino Sorsa
- Department of Psychology and Education, University of Jyväskylä, Finland
| | - Jarmo Hämäläinen
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Psychology and Education, University of Jyväskylä, Finland
| | - Hasse Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Clinical Medicine, Unit of Public Health, University of Turku, Finland
- Department of Child Psychiatry, Turku University Hospital, Turku, Finland
| | - Linnea Karlsson
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Clinical Medicine, Unit of Public Health, University of Turku, Finland
- Department of Child Psychiatry, Turku University Hospital, Turku, Finland
| | - Ekaterina Saukko
- Department of Radiology, Turku University Hospital and University of Turku, Turku, Finland
| | - Teija Sainio
- Department of Medical Physics, Turku University Hospital and University of Turku, Turku, Finland
| | - Arttu Peuna
- Department of Diagnostic Services, Hospital Nova of Central Finland, Wellbeing Services County of Central Finland, Finland
| | - Riikka Korja
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Psychology and Speech-Pathology, University of Turku, Finland
| | - Mikko Aro
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Education, University of Jyväskylä, Finland
| | - Paavo H.T. Leppänen
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Psychology and Education, University of Jyväskylä, Finland
| | - Jetro J. Tuulari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Turku Collegium for Science and Medicine, University of Turku, Turku, Finland
- Department of Psychiatry, Turku University Hospital and University of Turku, Turku, Finland
| | - Harri Merisaari
- FinnBrain Birth Cohort Study, Turku Brain and Mind Center, Department of Clinical Medicine, University of Turku, Turku, Finland
- Centre for Population Health Research, Turku University Hospital and University of Turku, Turku, Finland
- Centre of Excellence in Learning Dynamics and Intervention Research (InterLearn), University of Jyväskylä and University of Turku, Finland
- Department of Radiology, Turku University Hospital and University of Turku, Turku, Finland
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6
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Radunsky D, Solomon C, Stern N, Blumenfeld-Katzir T, Filo S, Mezer A, Karsa A, Shmueli K, Soustelle L, Duhamel G, Girard OM, Kepler G, Shrot S, Hoffmann C, Ben-Eliezer N. A comprehensive protocol for quantitative magnetic resonance imaging of the brain at 3 Tesla. PLoS One 2024; 19:e0297244. [PMID: 38820354 PMCID: PMC11142522 DOI: 10.1371/journal.pone.0297244] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Accepted: 01/01/2024] [Indexed: 06/02/2024] Open
Abstract
Quantitative MRI (qMRI) has been shown to be clinically useful for numerous applications in the brain and body. The development of rapid, accurate, and reproducible qMRI techniques offers access to new multiparametric data, which can provide a comprehensive view of tissue pathology. This work introduces a multiparametric qMRI protocol along with full postprocessing pipelines, optimized for brain imaging at 3 Tesla and using state-of-the-art qMRI tools. The total scan time is under 50 minutes and includes eight pulse-sequences, which produce range of quantitative maps including T1, T2, and T2* relaxation times, magnetic susceptibility, water and macromolecular tissue fractions, mean diffusivity and fractional anisotropy, magnetization transfer ratio (MTR), and inhomogeneous MTR. Practical tips and limitations of using the protocol are also provided and discussed. Application of the protocol is presented on a cohort of 28 healthy volunteers and 12 brain regions-of-interest (ROIs). Quantitative values agreed with previously reported values. Statistical analysis revealed low variability of qMRI parameters across subjects, which, compared to intra-ROI variability, was x4.1 ± 0.9 times higher on average. Significant and positive linear relationship was found between right and left hemispheres' values for all parameters and ROIs with Pearson correlation coefficients of r>0.89 (P<0.001), and mean slope of 0.95 ± 0.04. Finally, scan-rescan stability demonstrated high reproducibility of the measured parameters across ROIs and volunteers, with close-to-zero mean difference and without correlation between the mean and difference values (across map types, mean P value was 0.48 ± 0.27). The entire quantitative data and postprocessing scripts described in the manuscript are publicly available under dedicated GitHub and Figshare repositories. The quantitative maps produced by the presented protocol can promote longitudinal and multi-center studies, and improve the biological interpretability of qMRI by integrating multiple metrics that can reveal information, which is not apparent when examined using only a single contrast mechanism.
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Affiliation(s)
- Dvir Radunsky
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Chen Solomon
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | - Neta Stern
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
| | | | - Shir Filo
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Aviv Mezer
- The Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Anita Karsa
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Karin Shmueli
- Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | | | | | | - Gal Kepler
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- School of Neurobiology, Biochemistry and Biophysics, Faculty of Life Science, Tel Aviv University, Tel Aviv, Israel
| | - Shai Shrot
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Chen Hoffmann
- Sackler School of Medicine, Tel-Aviv University, Tel-Aviv, Israel
- Department of Diagnostic Imaging, Sheba Medical Center, Ramat-Gan, Israel
| | - Noam Ben-Eliezer
- Department of Biomedical Engineering, Tel-Aviv University, Tel Aviv, Israel
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Center for Advanced Imaging Innovation and Research (CAI2R), New-York University Langone Medical Center, New York, NY, United States of America
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7
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Yoon JA, Kang C, Park JS, You Y, Min JH, In YN, Jeong W, Ahn HJ, Jeong HS, Kim YH, Lee BK, Kim D. Quantitative analysis of apparent diffusion coefficients to predict neurological prognosis in cardiac arrest survivors: an observational derivation and internal-external validation study. Crit Care 2024; 28:138. [PMID: 38664807 PMCID: PMC11044301 DOI: 10.1186/s13054-024-04909-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 04/11/2024] [Indexed: 04/29/2024] Open
Abstract
BACKGROUND This study aimed to validate apparent diffusion coefficient (ADC) values and thresholds to predict poor neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors by quantitatively analysing the ADC values via brain magnetic resonance imaging (MRI). METHODS This observational study used prospectively collected data from two tertiary academic hospitals. The derivation cohort comprised 70% of the patients randomly selected from one hospital, whereas the internal validation cohort comprised the remaining 30%. The external validation cohort used the data from another hospital, and the MRI data were restricted to scans conducted at 3 T within 72-96 h after an OHCA experience. We analysed the percentage of brain volume below a specific ADC value at 50-step intervals ranging from 200 to 1200 × 10-6 mm2/s, identifying thresholds that differentiate between good and poor outcomes. Poor neurological outcomes were defined as cerebral performance categories 3-5, 6 months after experiencing an OHCA. RESULTS A total of 448 brain MRI scans were evaluated, including a derivation cohort (n = 224) and internal/external validation cohorts (n = 96/128, respectively). The proportion of brain volume with ADC values below 450, 500, 550, 600, and 650 × 10-6 mm2/s demonstrated good to excellent performance in predicting poor neurological outcomes in the derivation group (area under the curve [AUC] 0.89-0.91), and there were no statistically significant differences in performances among the derivation, internal validation, and external validation groups (all P > 0.5). Among these, the proportion of brain volume with an ADC below 600 × 10-6 mm2/s predicted a poor outcome with a 0% false-positive rate (FPR) and 76% (95% confidence interval [CI] 68-83) sensitivity at a threshold of > 13.2% in the derivation cohort. In both the internal and external validation cohorts, when using the same threshold, a specificity of 100% corresponded to sensitivities of 71% (95% CI 58-81) and 78% (95% CI 66-87), respectively. CONCLUSIONS In this validation study, by consistently restricting the MRI types and timing during quantitative analysis of ADC values in brain MRI, we observed high reproducibility and sensitivity at a 0% FPR. Prospective multicentre studies are necessary to validate these findings.
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Affiliation(s)
- Jung A Yoon
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Changshin Kang
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jung Soo Park
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea.
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
| | - Yeonho You
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Jin Hong Min
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, Daejoen, Republic of Korea
| | - Yong Nam In
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
- Department of Emergency Medicine, Chungnam National University Sejong Hospital, Daejoen, Republic of Korea
| | - Wonjoon Jeong
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hong Jun Ahn
- Department of Emergency Medicine, Chungnam National University Hospital, Daejoen, Republic of Korea
- Department of Emergency Medicine, College of Medicine, Chungnam National University, 266 Munwha-ro, Jung-gu, Daejeon, 35015, Republic of Korea
| | - Hye Seon Jeong
- Department of Neurology, Chungnam National University Hospital, Daejoen, Republic of Korea
| | - Yong Hwan Kim
- Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Gyeongsangnam-do, Republic of Korea
| | - Byung Kook Lee
- Department of Emergency Medicine, Chonnam National University Medical School, Chonnam National University Hospital, Gwangju, Republic of Korea
| | - Dongha Kim
- Department of Statistics, Sungshin Women's University, Seoul, Republic of Korea
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8
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Deuter D, Hense K, Kunkel K, Vollmayr J, Schachinger S, Wendl C, Schicho A, Fellner C, Salzberger B, Hitzenbichler F, Zeller J, Vielsmeier V, Dodoo-Schittko F, Schmidt NO, Rosengarth K. SARS-CoV2 evokes structural brain changes resulting in declined executive function. PLoS One 2024; 19:e0298837. [PMID: 38470899 DOI: 10.1371/journal.pone.0298837] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 01/30/2024] [Indexed: 03/14/2024] Open
Abstract
BACKGROUND Several research has underlined the multi-system character of COVID-19. Though effects on the Central Nervous System are mainly discussed as disease-specific affections due to the virus' neurotropism, no comprehensive disease model of COVID-19 exists on a neurofunctional base by now. We aimed to investigate neuroplastic grey- and white matter changes related to COVID-19 and to link these changes to neurocognitive testings leading towards a multi-dimensional disease model. METHODS Groups of acutely ill COVID-19 patients (n = 16), recovered COVID-19 patients (n = 21) and healthy controls (n = 13) were prospectively included into this study. MR-imaging included T1-weighted sequences for analysis of grey matter using voxel-based morphometry and diffusion-weighted sequences to investigate white matter tracts using probabilistic tractography. Comprehensive neurocognitive testing for verbal and non-verbal domains was performed. RESULTS Alterations strongly focused on grey matter of the frontal-basal ganglia-thalamus network and temporal areas, as well as fiber tracts connecting these areas. In acute COVID-19 patients, a decline of grey matter volume was found with an accompanying diminution of white matter tracts. A decline in executive function and especially verbal fluency was found in acute patients, partially persisting in recovered. CONCLUSION Changes in gray matter volume and white matter tracts included mainly areas involved in networks of executive control and language. Deeper understanding of these alterations is necessary especially with respect to long-term impairments, often referred to as 'Post-COVID'.
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Affiliation(s)
- Daniel Deuter
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Katharina Hense
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Kevin Kunkel
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Johanna Vollmayr
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Sebastian Schachinger
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Christina Wendl
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
- Institut für Neuroradiologie, Medbo Bezirksklinikum Regensburg, Regensburg, Germany
| | - Andreas Schicho
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
| | - Claudia Fellner
- Institut für Röntgendiagnostik, University Hospital Regensburg, Regensburg, Germany
| | - Bernd Salzberger
- Abteilung für Krankenhaushygiene und Infektiologie, University Hospital Regensburg, Regensburg, Germany
| | - Florian Hitzenbichler
- Abteilung für Krankenhaushygiene und Infektiologie, University Hospital Regensburg, Regensburg, Germany
| | - Judith Zeller
- Klinik und Poliklinik für Innere Medizin II, University Hospital Regensburg, Regensburg, Germany
| | - Veronika Vielsmeier
- Klinik und Poliklinik für Hals-Nasen-Ohren-Heilkunde, University Hospital Regensburg, Regensburg, Germany
| | - Frank Dodoo-Schittko
- Institut für Sozialmedizin und Gesundheitsforschung, Otto von Guericke University Magdeburg, Magdeburg, Germany
| | - Nils Ole Schmidt
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
| | - Katharina Rosengarth
- Klinik und Poliklinik für Neurochirurgie, University Hospital Regensburg, Regensburg, Germany
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Rasmussen AM, Friismose AI, Mussmann B, Lagerstrand K, Harbo FSG, Jensen J. Repeatability of diffusion-based stiffness prediction - A healthy volunteer study. Radiography (Lond) 2024; 30:524-530. [PMID: 38262191 DOI: 10.1016/j.radi.2024.01.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 01/03/2024] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
INTRODUCTION The study investigated the repeatability of brain diffusion-based stiffness prediction (DWIstiff) in healthy volunteers. METHODS Thirty-one healthy volunteers were examined with DWIstiff using two different sets of b-values: b200-1500 s/mm2 (DWIstiff, 1500) and b200-1000 s/mm2 (DWIstiff, 1000). Each b-value set was scanned twice per imaging session without repositioning the participants. DWIstiff images were reconstructed from each set. Two observers delineated regions of interest (ROIs) on each DWIstiff image. The repeatability coefficient (RC), coefficient of variation (CV), inter- and intraobserver agreement were calculated. RESULTS After excluding three participants due to image artifacts, the study included twenty-eight volunteers (mean age (range)) 37 years (24-62), 10 males, 18 females). For DWIstiff, 1500, the lowest and the highest RCs were in the parietal lobe (0.52) and respectively the brain stem (1.17). The lowest RC for DWIstiff, 1000 was in the frontal lobe (0.42) and the highest in the brain stem (1.58). The CV for whole brain measurements was 3.83 % for DWIstiff, 1500 and 4.93 % for DWIstiff, 1000. The Bland‒Altman (BA) limits of agreement (LoA) for the intraobserver agreement of DWIstiff, 1500 were -0.90 to 1.06 and respectively -0.78 to 0.88 for DWIstiff, 1000. Regarding interobserver agreement, the LoA were -0.85 to 0.94 for DWIstiff, 1500 and -0.61 to 0.66 for DWIstiff, 1000. CONCLUSION DWIstiff is a precise technique with some observer dependence. Repeatability is higher for DWIstiff, 1000 s/mm2 than for DWIstiff 1500 s/mm2. IMPLICATIONS FOR PRACTICE Our findings suggest that DWIstiff can reliably detect stiffness changes larger than 4.93 % in healthy volunteers. Further studies should investigate whether the repeatability of DWIstiff may be affected by the presence of pathology such as a brain tumor.
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Affiliation(s)
- A-M Rasmussen
- Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark
| | - A I Friismose
- Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark.
| | - B Mussmann
- Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark; Department of Life Sciences and Health, Radiography, Oslo Metropolitan University, Oslo, Norway
| | - K Lagerstrand
- Department of Medical Physics and Biomedical Engineering, Sahlgrenska University Hospital, Gothenburg, Sweden; Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - F S G Harbo
- Department of Radiology, Odense University Hospital, Odense, Denmark
| | - J Jensen
- Department of Radiology, Odense University Hospital, Odense, Denmark; Research and Innovation Unit of Radiology, University of Southern Denmark, Odense, Denmark
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10
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Pandey A, Mohseni A, Shaghaghi M, Pandey P, Rezvani Habibabadi R, Hazhirkarzar B, Ly A, Panid Madani S, Borhani A, Kamel IR. Incremental value of volumetric multiparametric MRI over Fudan score for prognosis of unresectable intrahepatic cholangiocarcinoma treated with systemic chemotherapy. Eur J Radiol 2024; 170:111196. [PMID: 38029705 DOI: 10.1016/j.ejrad.2023.111196] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Revised: 10/25/2023] [Accepted: 11/13/2023] [Indexed: 12/01/2023]
Abstract
BACKGROUND Individualized patient care requires prognostic models customized to a tumor and an individual's disease profile for reliable survival prediction. MRI has prognostic value for intrahepatic cholangiocarcinoma (ICCA). Existing prognostic models for ICCA exclude imaging-based information about an individual's tumor that may reflect important aspects of tumor's biology. Fudan score, a prognostic model applicable to unresectable ICCA, is limited by subjective morphologic imaging parameters. OBJECTIVES To assess the prognostic value of baseline volumetric multiparametric MRI in unresectable intrahepatic cholangiocarcinoma (ICCA) treated with systemic chemotherapy and the incremental value of MRI over the Fudan score. METHODS This retrospective study included 114 ICCA patients treated with systemic chemotherapy between 2007 and 2021 after a baseline MRI. The single largest tumor was volumetrically assessed for anatomic (total tumor volume and diameter) and functional parameters (viable tumor volume, percentage-viable tumor volume, viable tumor burden, and ADC). A derivation cohort of 30 patients was utilized to identify MRI parameters associated with overall survival (OS) using Cox regression analysis. The incremental value of MRI over Fudan score was assessed on an independent sub-cohort of 84 patients using Kaplan-Meier analysis and C-index. RESULTS 114 patients (64 years +/- 11; 61 women) were evaluated. Pre-treatment high (>1350x10-6 mm2/sec) ADC was the only independent predictor of OS (HR, 8.07; P < 0.001). Replacing subjective tumor boundary with objective ADC value, and using modified biochemical thresholds increased the prognostic stratification for the risk groups in the modified ADC-Fudan model compared to the original Fudan model (median survival 12 and 4.5 months; P = 0.055; vs. 11 and 3 months; P < 0.001). The modified ADC-Fudan model demonstrated an 11 % improvement over the original Fudan model (c-index: 0.80 vs. 0.69; P = 0.044) for survival prediction. CONCLUSIONS High pre-treatment volumetric ADC was associated with unfavorable prognosis in patients with unresectable intrahepatic cholangiocarcinoma treated with systemic chemotherapy. Supplementing the original Fudan model with ADC and modified serum marker thresholds improved the survival prediction performance by 11% in the resulting modified ADC-Fudan model. CLINICAL IMPACT Volumetric MRI could improve the survival prediction among ICCA patients prior to receiving potentially toxic and expensive palliative chemotherapies. This could potentially guide individualized therapy for this patient cohort.
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Affiliation(s)
- Ankur Pandey
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA; Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland Medical Center, 22 S Greene St, Baltimore, MD 21201, USA.
| | - Alireza Mohseni
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Mohammadreza Shaghaghi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Pallavi Pandey
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA
| | - Roya Rezvani Habibabadi
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Bita Hazhirkarzar
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Andrew Ly
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Seyedeh Panid Madani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Ali Borhani
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
| | - Ihab R Kamel
- Russell H. Morgan Department of Radiology and Radiological Sciences, Johns Hopkins University School of Medicine, 600 N Wolfe St, Room 143, Baltimore, MD 21287, USA.
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Schillmaier M, Kaika A, Topping GJ, Braren R, Schilling F. Repeatability and reproducibility of apparent exchange rate measurements in yeast cell phantoms using filter-exchange imaging. MAGMA (NEW YORK, N.Y.) 2023; 36:957-974. [PMID: 37436611 PMCID: PMC10667135 DOI: 10.1007/s10334-023-01107-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 07/13/2023]
Abstract
OBJECTIVES Development of a protocol for validation and quality assurance of filter-exchange imaging (FEXI) pulse sequences with well-defined and reproducible phantoms. MATERIALS AND METHODS A FEXI pulse sequence was implemented on a 7 T preclinical MRI scanner. Six experiments in three different test categories were established for sequence validation, demonstration of the reproducibility of phantoms and the measurement of induced changes in the apparent exchange rate (AXR). First, an ice-water phantom was used to investigate the consistency of apparent diffusion coefficient (ADC) measurements with different diffusion filters. Second, yeast cell phantoms were utilized to validate the determination of the AXR in terms of repeatability (same phantom and session), reproducibility (separate but comparable phantoms in different sessions) and directionality of diffusion encodings. Third, the yeast cell phantoms were, furthermore, used to assess potential AXR bias because of altered cell density and temperature. In addition, a treatment experiment with aquaporin inhibitors was performed to evaluate the influence of these compounds on the cell membrane permeability in yeast cells. RESULTS FEXI-based ADC measurements of an ice-water phantom were performed for three different filter strengths, showed good agreement with the literature value of 1.099 × 10-3 mm2/s and had a maximum coefficient of variation (CV) of 0.55% within the individual filter strengths. AXR estimation in a single yeast cell phantom and imaging session with five repetitions resulted in an overall mean value of (1.49 ± 0.05) s-1 and a CV of 3.4% between the chosen regions of interest. For three separately prepared phantoms, AXR measurements resulted in a mean value of (1.50 ± 0.04) s-1 and a CV of 2.7% across the three phantoms, demonstrating high reproducibility. Across three orthogonal diffusion directions, a mean value of (1.57 ± 0.03) s-1 with a CV of 1.9% was detected, consistent with isotropy of AXR in yeast cells. Temperature and AXR were linearly correlated (R2 = 0.99) and an activation energy EA of 37.7 kJ/mol was determined by Arrhenius plot. Furthermore, a negative correlation was found between cell density (as determined by the reference ADC/fe) and AXR (R2 = 0.95). The treatment experiment resulted in significantly decreased AXR values at different temperatures in the treated sample compared to the untreated control indicating an inhibiting effect. CONCLUSIONS Using ice-water and yeast cell-based phantoms, a protocol for the validation of FEXI pulse sequences was established for the assessment of stability, repeatability, reproducibility and directionality. In addition, a strong dependence of AXR on cell density and temperature was shown. As AXR is an emerging novel imaging biomarker, the suggested protocol will be useful for quality assurance of AXR measurements within a study and potentially across multiple sites.
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Affiliation(s)
- Mathias Schillmaier
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
- Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Athanasia Kaika
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Geoffrey J Topping
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany
| | - Rickmer Braren
- Diagnostic and Interventional Radiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany.
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
| | - Franz Schilling
- Department of Nuclear Medicine, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675, Munich, Germany.
- German Cancer Consortium (DKTK), Partner Site Munich and German Cancer Research Center (DKFZ), Heidelberg, Germany.
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12
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Saito Y, Kamagata K, Andica C, Maikusa N, Uchida W, Takabayashi K, Yoshida S, Hagiwara A, Fujita S, Akashi T, Wada A, Irie R, Shimoji K, Hori M, Kamiya K, Koike S, Hayashi T, Aoki S. Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping. Aging Dis 2023:AD.2023.1020. [PMID: 38029401 DOI: 10.14336/ad.2023.1020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Christina Andica
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Seina Yoshida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Ryusuke Irie
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Keigo Shimoji
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo Japan
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center, Tokyo Japan
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Japan
- Department of Brain Connectomics, Kyoto University Graduate School of Medicine
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
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13
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Lawrence LSP, Chan RW, Chen H, Stewart J, Ruschin M, Theriault A, Myrehaug S, Detsky J, Maralani PJ, Tseng CL, Soliman H, Jane Lim-Fat M, Das S, Stanisz GJ, Sahgal A, Lau AZ. Diffusion-weighted imaging on an MRI-linear accelerator to identify adversely prognostic tumour regions in glioblastoma during chemoradiation. Radiother Oncol 2023; 188:109873. [PMID: 37640160 DOI: 10.1016/j.radonc.2023.109873] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Revised: 07/12/2023] [Accepted: 08/20/2023] [Indexed: 08/31/2023]
Abstract
BACKGROUND AND PURPOSE Survival in glioblastoma might be extended by escalating the radiotherapy dose to treatment-resistant tumour and adapting to tumour changes. Diffusion-weighted imaging (DWI) on MRI-linear accelerators (MR-Linacs) could be used to identify a dose escalation target, but its prognostic value must be demonstrated. The purpose of this study was to determine whether MR-Linac DWI can assess treatment response in glioblastoma and whether changes in DWI show greater prognostic value than changes in the contrast-enhancing gross tumour volume (GTV). MATERIALS AND METHODS Seventy-five patients with glioblastoma were treated with chemoradiotherapy, of which 32 were treated on a 1.5 T MRI-linear accelerator (MR-Linac). Patients were imaged with simulation MRI scanners (MR-sim) at treatment planning and weeks 2, 4, and 10 after treatment start. Twenty-eight patients had additional MR-Linac DWI sequences. Cox modelling was used to evaluate the correlation of overall and progression-free survival (OS and PFS) with clinical variables and volumetric changes in the GTV and low-ADC regions (ADC < 1.25 µm2/ms within GTV). RESULTS In total, 479 MR-Linac DWI and 289 MR-sim DWI datasets were analyzed. MR-Linac low-ADC changes between weeks 2 and 5 inclusive were prognostic for OS (hazard ratio lower limits ≥ 1.2, p-values ≤ 0.02). MR-sim low-ADC changes showed greater correlation with OS and PFS than GTV changes (e.g., OS hazard ratio at week 2 was 3.4 (p <0.001) for low-ADC versus 2.0 (p = 0.022) for GTV). CONCLUSION MR-Linac DWI can measure low-ADC tumour volumes that correlate with OS and PFS better than contrast-enhancing GTV. Low-ADC regions could serve as dose escalation targets.
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Affiliation(s)
| | - Rachel W Chan
- Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Hanbo Chen
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - James Stewart
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mark Ruschin
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Aimee Theriault
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sten Myrehaug
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Jay Detsky
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Pejman J Maralani
- Medical Imaging, University of Toronto, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada
| | - Chia-Lin Tseng
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Hany Soliman
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Sunit Das
- Keenan Chair in Surgery, St. Michael's Hospital, Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada
| | - Greg J Stanisz
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada; Department of Neurosurgery and Paediatric Neurosurgery, Medical University, Lublin, Poland
| | - Arjun Sahgal
- Department of Radiation Oncology, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - Angus Z Lau
- Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Physical Sciences, Sunnybrook Research Institute, Toronto, Ontario, Canada.
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14
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Eldirdiri A, Zhuo J, Lin Z, Lu H, Gullapalli RP, Jiang D. Toward vendor-independent measurement of cerebral venous oxygenation: Comparison of TRUST MRI across three major MRI manufacturers and association with end-tidal CO 2. NMR IN BIOMEDICINE 2023; 36:e4990. [PMID: 37315951 PMCID: PMC10801912 DOI: 10.1002/nbm.4990] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/16/2023]
Abstract
Cerebral venous oxygenation (Yv ) is a valuable biomarker for a variety of brain diseases. T2 relaxation under spin tagging (TRUST) MRI is a widely used method for Yv quantification. In this work, there were two main objectives. The first was to evaluate the reproducibility of TRUST Yv measurements across MRI scanners from different vendors. The second was to examine the correlation between Yv and end-tidal CO2 (EtCO2 ) in a multisite, multivendor setting and determine the usefulness of this correlation to account for variations in Yv caused by normal variations and physiological fluctuations. Standardized TRUST pulse sequences were implemented on three scanners from major MRI vendors (GE, Siemens, Philips). These scanners were located at two research institutions. Ten healthy subjects were scanned. On each scanner, the subject underwent two scan sessions, each of which included three TRUST scans, to evaluate the intrasession and intersession reproducibility of Yv . Each scanner was also equipped with a capnograph device to record the EtCO2 of the subject during the MRI scan. We found no significant bias in Yv measurements across the three scanners (P = 0.18). The measured Yv values on the three scanners were also strongly correlated with each other (intraclass correlation coefficients > 0.85, P < 0.001). The intrasession and intersession coefficients of variation of Yv were less than 4% and showed no significant difference among the scanners. In addition, our results revealed that (1) within the same subject, Yv increased with EtCO2 at a rate of 1.24 ± 0.17%/mmHg (P < 0.0001), and (2) across different subjects, individuals with a higher EtCO2 had a higher Yv , at a rate of 0.94 ± 0.36%/mmHg (P = 0.01). These results suggest that (1) the standardized TRUST sequences had similar accuracies and reproducibilities for the quantification of Yv across the scanners, and (2) recording of EtCO2 may be a useful complement to Yv measurement to account for CO2 -related physiological fluctuations in Yv in multisite, multivendor studies.
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Affiliation(s)
- Abubakr Eldirdiri
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Jiachen Zhuo
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Zixuan Lin
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hanzhang Lu
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Research Institute, Baltimore, Maryland, USA
| | - Rao P. Gullapalli
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, Maryland, United States of America
| | - Dengrong Jiang
- The Russell H. Morgan Department of Radiology & Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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15
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Zakaria R, Jenkinson MD, Radon M, Das K, Poptani H, Rathi N, Rudland PS. Immune checkpoint inhibitor treatment of brain metastasis associated with a less invasive growth pattern, higher T-cell infiltration and raised tumor ADC on diffusion weighted MRI. Cancer Immunol Immunother 2023; 72:3387-3393. [PMID: 37477652 PMCID: PMC10491542 DOI: 10.1007/s00262-023-03499-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 07/10/2023] [Indexed: 07/22/2023]
Abstract
BACKGROUND Brain metastases are the most common intracranial tumors with an increasing incidence. They are an important cause of morbidity and mortality in patients with solid organ cancer and a focus of recent clinical research and experimental interest. Immune checkpoint inhibitors are being increasingly used to treat solid organ cancers. METHODS To determine whether immune checkpoint inhibitors were biologically effective in the brain, we compared melanoma brain metastasis samples where treatment with ipilimumab had occurred preoperatively to those who had not received any immune modulating therapy and looked for histopathological (invasion, vascularity, metastasis inducing proteins, matrix metalloproteinases, immune cell infiltration, tissue architecture) and advanced MRI differences (diffusion weighted imaging). RESULTS Co-localized tissue samples from the same regions as MRI regions of interest showed significantly lower vascularity (density of CD34 + vessels) in the core and higher T-cell infiltration (CD3 + cells) in the leading edge for ipilimumab-treated brain metastasis samples than for untreated cases and this correlated with a higher tumor ADC signal at post-treatment/preoperative MRI brain. CONCLUSIONS Treatment of a melanoma brain metastasis with ipilimumab appears to cause measurable biological changes in the tumor that can be correlated with post-treatment diffusion weighted MRI imaging, suggesting both a mechanism of action and a possible surrogate marker of efficacy.
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Affiliation(s)
- Rasheed Zakaria
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Nuffield Building, Crown Street, Liverpool, L69 3BX, UK.
| | - Michael D Jenkinson
- Department of Pharmacology and Therapeutics, University of Liverpool, Liverpool, UK
| | - Mark Radon
- Department of Neuroradiology, Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Kumar Das
- Department of Neuroradiology, Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Harish Poptani
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Nuffield Building, Crown Street, Liverpool, L69 3BX, UK
| | - Nitika Rathi
- Department of Neuropathology, Walton Centre NHS Foundation Trust, Liverpool, UK
| | - Philip S Rudland
- Department of Molecular and Clinical Cancer Medicine, University of Liverpool, Nuffield Building, Crown Street, Liverpool, L69 3BX, UK
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16
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Gupta L, Peterson EL, Williams C, Altman E, Harpole R, Martin DJ, Escott EJ, Timoney PJ, Prendes MA. Diffusion-Weighted Imaging of the Orbit: A Case Series and Systematic Review. Ophthalmic Plast Reconstr Surg 2023; 39:407-418. [PMID: 36757844 DOI: 10.1097/iop.0000000000002325] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
PURPOSE To describe the findings of diffusion-weighted imaging (DWI) for a series of orbital lesions and provide a systematic review of relevant literature. METHODS A retrospective review of 20 patients with orbital lesions who underwent MRI with DWI at two academic institutions between 2015 and 2020 was performed. Lesion diagnosis was histopathologically confirmed except a presumed cavernous hemangioma. Echoplanar diffusion-weighted images had been acquired using 2 or 3 b values (b=0 and 1000 or b=0, 500, and 1000) at 1.5T or 3T. Lesions with significant artifacts were excluded. DWI sequences were analyzed by neuro-radiologists blinded to the diagnosis. Mean ADC values of lesions were calculated from a single region of interest. An independent two-tailed t test was used to compare categories of lesions with p < 0.05 considered significant. A systematic review of the literature was performed. RESULTS Our study included 21 lesions. ADC values were significantly lower for malignant lesions (0.628 ± 0.125 × 10 -3 mm 2 /s) than inflammatory lesions (1.167 ± 0.381 × 10 -3 mm 2 /s) ( p < 0.001). ADC values were significantly lower for orbital lymphoma (mean 0.621 ± 0.147 × 10 -3 mm 2 /s) than idiopathic orbital inflammation (mean 1.188 ± 0.269 × 10 -3 mm 2 /s) with no overlap ( p < 0.001). CONCLUSIONS Orbital malignancies demonstrated lower ADC values, while inflammatory processes demonstrated higher ADC values, except IgG4-related disease. DWI and ADC values differentiated idiopathic orbital inflammation from orbital lymphoma. This study highlights the role of DWI in evaluating orbital pathology.
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Affiliation(s)
- Lalita Gupta
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio
| | - Eric L Peterson
- Department of Ophthalmology, University of Kentucky, Lexington, Kentucky
| | - Cody Williams
- Department of Ophthalmology, University of Kentucky, Lexington, Kentucky
| | - Emily Altman
- Department of Ophthalmology, University of Kentucky, Lexington, Kentucky
| | - Ryan Harpole
- Department of Ophthalmology, University of Kentucky, Lexington, Kentucky
| | - Douglas J Martin
- Department of Radiology, Case Western Reserve University, Cleveland, Ohio
| | - Edward J Escott
- Department of Radiology, Division of Neuroradiology, University of Kentucky Medical Center, Lexington, Kentucky
| | - Peter J Timoney
- Department of Ophthalmology, University of Kentucky, Lexington, Kentucky
| | - Mark A Prendes
- Department of Ophthalmology and Visual Sciences, Case Western Reserve University, Cleveland, Ohio
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17
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Yang N, Xiao X, Gu G, Wang X, Zhang X, Wang Y, Pan C, Zhang P, Ma L, Zhang L, Liao H. Diffusion MRI-based connectomics features improve the noninvasive prediction of H3K27M mutation in brainstem gliomas. Radiother Oncol 2023; 186:109789. [PMID: 37414255 DOI: 10.1016/j.radonc.2023.109789] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/02/2023] [Accepted: 06/28/2023] [Indexed: 07/08/2023]
Abstract
PURPOSE To establish an individualized predictive model to identify patients with brainstem gliomas (BSGs) at high risk of H3K27M mutation, with the inclusion of brain structural connectivity analysis based on diffusion MRI (dMRI). MATERIALS AND METHODS A primary cohort of 133 patients with BSGs (80 H3K27M-mutant) were retrospectively included. All patients underwent preoperative conventional MRI and dMRI. Tumor radiomics features were extracted from conventional MRI, while two kinds of global connectomics features were extracted from dMRI. A machine learning-based individualized H3K27M mutation prediction model combining radiomics and connectomics features was generated with a nested cross validation strategy. Relief algorithm and SVM method were used in each outer LOOCV loop to select the most robust and discriminative features. Additionally, two predictive signatures were established using the LASSO method, and simplified logistic models were built using multivariable logistic regression analysis. An independent cohort of 27 patients was used to validate the best model. RESULTS 35 tumor-related radiomics features, 51 topological properties of brain structural connectivity networks, and 11 microstructural measures along white matter tracts were selected to construct a machine learning-based H3K27M mutation prediction model, which achieved an AUC of 0.9136 in the independent validation set. Radiomics- and connectomics-based signatures were generated and simplified combined logistic model was built, upon which derived nomograph achieved an AUC of 0.8827 in the validation cohort. CONCLUSION dMRI is valuable in predicting H3K27M mutation in BSGs, and connectomics analysis is a promising approach. Combining multiple MRI sequences and clinical features, the established models have good performance.
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Affiliation(s)
- Ne Yang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3)
| | - Xiong Xiao
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Guocan Gu
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Xianyu Wang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3)
| | - Xinran Zhang
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3)
| | - Yi Wang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Changcun Pan
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Peng Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Longfei Ma
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3)
| | - Liwei Zhang
- Department of Neurosurgery, Beijing Tiantan Hospital, Capital Medical University, China(4); China National Clinical Research Center for Neurological Diseases, China(4); Beijing Neurosurgical Institute, Beijing Tiantan Hospital, Capital Medical University, China(4)
| | - Hongen Liao
- Department of Biomedical Engineering, School of Medicine, Tsinghua University, China(3).
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18
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Valcourt Caron A, Shmuel A, Hao Z, Descoteaux M. versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Front Neuroinform 2023; 17:1191200. [PMID: 37637471 PMCID: PMC10449583 DOI: 10.3389/fninf.2023.1191200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/27/2023] [Indexed: 08/29/2023] Open
Abstract
The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.
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Affiliation(s)
- Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Amir Shmuel
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ziqi Hao
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
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19
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Hu C, Grech‐Sollars M, Statton B, Li Z, Gao F, Williams GR, Parker GJM, Zhou F. Direct jet coaxial electrospinning of axon-mimicking fibers for diffusion tensor imaging. POLYM ADVAN TECHNOL 2023; 34:2573-2584. [PMID: 38505514 PMCID: PMC10946859 DOI: 10.1002/pat.6073] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Revised: 04/01/2023] [Accepted: 04/16/2023] [Indexed: 03/21/2024]
Abstract
Hollow polymer microfibers with variable microstructural and hydrophilic properties were proposed as building elements to create axon-mimicking phantoms for validation of diffusion tensor imaging (DTI). The axon-mimicking microfibers were fabricated in a mm-thick 3D anisotropic fiber strip, by direct jet coaxial electrospinning of PCL/polysiloxane-based surfactant (PSi) mixture as shell and polyethylene oxide (PEO) as core. Hydrophilic PCL-PSi fiber strips were first obtained by carefully selecting appropriate solvents for the core and appropriate fiber collector rotating and transverse speeds. The porous cross-section and anisotropic orientation of axon-mimicking fibers were then quantitatively evaluated using two ImageJ plugins-nearest distance (ND) and directionality based on their scanning electron microscopy (SEM) images. Third, axon-mimicking phantom was constructed from PCL-PSi fiber strips with variable porous-section and fiber orientation and tested on a 3T clinical MR scanner. The relationship between DTI measurements (mean diffusivity [MD] and fractional anisotropy [FA]) of phantom samples and their pore size and fiber orientation was investigated. Two key microstructural parameters of axon-mimicking phantoms including normalized pore distance and dispersion of fiber orientation could well interpret the variations in DTI measurements. Two PCL-PSi phantom samples made from different regions of the same fiber strips were found to have similar MD and FA values, indicating that the direct jet coaxial electrospun fiber strips had consistent microstructure. More importantly, the MD and FA values of the developed axon-mimicking phantoms were mostly in the biologically relevant range.
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Affiliation(s)
- Chunyan Hu
- College of Textiles and ClothingQingdao UniversityQingdaoChina
| | - Matthew Grech‐Sollars
- Department of Computer ScienceUniversity College LondonLondonUK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and NeurosurgeryUniversity College London Hospitals NHS Foundation TrustLondonUK
| | - Ben Statton
- Medical Research Council, London Institute of Medical SciencesImperial College LondonLondonUK
| | - Zhanxiong Li
- College of Textile and Clothing EngineeringSoochow UniversitySuzhouChina
| | - Fei Gao
- Department of Radiology, Shandong Provincial Hospital, Cheeloo College of MedicineShandong UniversityJinanChina
| | | | - Geoff J. M. Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
- Bioxydyn LimitedManchesterUK
| | - Feng‐Lei Zhou
- College of Textiles and ClothingQingdao UniversityQingdaoChina
- School of PharmacyUniversity College LondonLondonUK
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
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20
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Campaña Perilla LA, Mahecha Carvajal ME, Cardona Ortegón JD, Barragán Corrales C, Barrera Patiño AM. Diffusion Tensor Imaging Protocol: The Need for Standardization of Measures. Radiology 2023; 308:e230470. [PMID: 37606576 PMCID: PMC10477501 DOI: 10.1148/radiol.230470] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/23/2023]
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21
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Mahani FSN, Kalantari A, Fink GR, Hoehn M, Aswendt M. A systematic review of the relationship between magnetic resonance imaging based resting-state and structural networks in the rodent brain. Front Neurosci 2023; 17:1194630. [PMID: 37554291 PMCID: PMC10405456 DOI: 10.3389/fnins.2023.1194630] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/05/2023] [Indexed: 08/10/2023] Open
Abstract
Recent developments in rodent brain imaging have enabled translational characterization of functional and structural connectivity at the whole brain level in vivo. Nevertheless, fundamental questions about the link between structural and functional networks remain unsolved. In this review, we systematically searched for experimental studies in rodents investigating both structural and functional network measures, including studies correlating functional connectivity using resting-state functional MRI with diffusion tensor imaging or viral tracing data. We aimed to answer whether functional networks reflect the architecture of the structural connectome, how this reciprocal relationship changes throughout a disease, how structural and functional changes relate to each other, and whether changes follow the same timeline. We present the knowledge derived exclusively from studies that included in vivo imaging of functional and structural networks. The limited number of available reports makes it difficult to draw general conclusions besides finding a spatial and temporal decoupling between structural and functional networks during brain disease. Data suggest that when overcoming the currently limited evidence through future studies with combined imaging in various disease models, it will be possible to explore the interaction between both network systems as a disease or recovery biomarker.
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Affiliation(s)
- Fatemeh S. N. Mahani
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Aref Kalantari
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Gereon R. Fink
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
| | - Mathias Hoehn
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
| | - Markus Aswendt
- Cognitive Neuroscience, Institute of Neuroscience and Medicine (INM-3), Research Centre Juelich, Juelich, Germany
- Department of Neurology, Faculty of Medicine and University Hospital, University of Cologne, Cologne, Germany
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22
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Cornell I, Al Busaidi A, Wastling S, Anjari M, Cwynarski K, Fox CP, Martinez-Calle N, Poynton E, Maynard J, Thust SC. Early MRI Predictors of Relapse in Primary Central Nervous System Lymphoma Treated with MATRix Immunochemotherapy. J Pers Med 2023; 13:1182. [PMID: 37511795 PMCID: PMC10381964 DOI: 10.3390/jpm13071182] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2023] [Revised: 07/14/2023] [Accepted: 07/17/2023] [Indexed: 07/30/2023] Open
Abstract
Primary Central Nervous System Lymphoma (PCNSL) is a highly malignant brain tumour. We investigated dynamic changes in tumour volume and apparent diffusion coefficient (ADC) measurements for predicting outcome following treatment with MATRix chemotherapy in PCNSL. Patients treated with MATRix (n = 38) underwent T1 contrast-enhanced (T1CE) and diffusion-weighted imaging (DWI) before treatment, after two cycles and after four cycles of chemotherapy. Response was assessed using the International PCNSL Collaborative Group (IPCG) imaging criteria. ADC histogram parameters and T1CE tumour volumes were compared among response groups, using one-way ANOVA testing. Logistic regression was performed to examine those imaging parameters predictive of response. Response after two cycles of chemotherapy differed from response after four cycles; of the six patients with progressive disease (PD) after four cycles of treatment, two (33%) had demonstrated a partial response (PR) or complete response (CR) after two cycles. ADCmean at baseline, T1CE at baseline and T1CE percentage volume change differed between response groups (0.005 < p < 0.038) and were predictive of MATRix treatment response (area under the curve: 0.672-0.854). Baseline ADC and T1CE metrics are potential biomarkers for risk stratification of PCNSL patients early during remission induction therapy with MATRix. Standard interim response assessment (after two cycles) according to IPCG imaging criteria does not reliably predict early disease progression in the context of a conventional treatment approach.
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Affiliation(s)
- Isabel Cornell
- UCL Institute of Neurology, Department of Brain Rehabilitation and Repair, Queen Square, London WC1N 3BG, UK
| | - Ayisha Al Busaidi
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3BG, UK
- Neuroradiology Department, Kings College Hospital NHS Foundation Trust, London SE5 9RS, UK
| | - Stephen Wastling
- UCL Institute of Neurology, Department of Brain Rehabilitation and Repair, Queen Square, London WC1N 3BG, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3BG, UK
| | - Mustafa Anjari
- UCL Institute of Neurology, Department of Brain Rehabilitation and Repair, Queen Square, London WC1N 3BG, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3BG, UK
- Radiology Department, Royal Free London NHS Foundation Trust, London NW3 2QG, UK
| | - Kate Cwynarski
- Haematology Department, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
| | - Christopher P Fox
- School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
| | | | - Edward Poynton
- Haematology Department, University College London Hospitals NHS Foundation Trust, London NW1 2BU, UK
| | - John Maynard
- UCL Institute of Neurology, Department of Brain Rehabilitation and Repair, Queen Square, London WC1N 3BG, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, London WC1N 3BG, UK
| | - Steffi C Thust
- UCL Institute of Neurology, Department of Brain Rehabilitation and Repair, Queen Square, London WC1N 3BG, UK
- Precision Imaging Beacon, School of Medicine, University of Nottingham, Nottingham NG7 2UH, UK
- Neuroradiology Department, Nottingham University Hospitals NHS Trust, Nottingham NG7 2UH, UK
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23
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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24
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Taraku B, Zavaliangos-Petropulu A, Loureiro JR, Al-Sharif NB, Kubicki A, Joshi SH, Woods RP, Espinoza R, Narr KL, Sahib AK. White matter microstructural perturbations after total sleep deprivation in depression. Front Psychiatry 2023; 14:1195763. [PMID: 37457774 PMCID: PMC10345348 DOI: 10.3389/fpsyt.2023.1195763] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2023] [Accepted: 06/06/2023] [Indexed: 07/18/2023] Open
Abstract
Background Total sleep deprivation (TSD) transiently reverses depressive symptoms in a majority of patients with depression. How TSD modulates diffusion tensor imaging (DTI) measures of white matter (WM) microstructure, which may be linked with TSD's rapid antidepressant effects, remains uncharacterized. Methods Patients with depression (N = 48, mean age = 33, 26 women) completed diffusion-weighted imaging and Hamilton Depression Rating (HDRS) and rumination scales before and after >24 h of TSD. Healthy controls (HC) (N = 53, 23 women) completed the same assessments at baseline, and after receiving TSD in a subset of HCs (N = 15). Tract based spatial statistics (TBSS) investigated voxelwise changes in fractional anisotropy (FA) across major WM pathways pre-to-post TSD in patients and HCs and between patients and HCs at baseline. Post hoc analyses tested for TSD effects for other diffusion metrics, and the relationships between change in diffusion measures with change in mood and rumination symptoms. Results Significant improvements in mood and rumination occurred in patients with depression (both p < 0.001), but not in HCs following TSD. Patients showed significant (p < 0.05, corrected) decreases in FA values in multiple WM tracts, including the body of the corpus callosum and anterior corona radiata post-TSD. Significant voxel-level changes in FA were not observed in HCs who received TSD (p > 0.05). However, differential effects of TSD between HCs and patients were found in the superior corona radiata, frontal WM and the posterior thalamic radiation (p < 0.05, corrected). A significant (p < 0.05) association between change in FA and axial diffusivity within the right superior corona radiata and improvement in rumination was found post-TSD in patients. Conclusion Total sleep deprivation leads to rapid microstructural changes in WM pathways in patients with depression that are distinct from WM changes associated with TSD observed in HCs. WM tracts including the superior corona radiata and posterior thalamic radiation could be potential biomarkers of the rapid therapeutic effects of TSD. Changes in superior corona radiata FA, in particular, may relate to improvements in maladaptive rumination.
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Affiliation(s)
- Brandon Taraku
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Artemis Zavaliangos-Petropulu
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Joana R. Loureiro
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Noor B. Al-Sharif
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Antoni Kubicki
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Shantanu H. Joshi
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
| | - Roger P. Woods
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Randall Espinoza
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Katherine L. Narr
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, United States
| | - Ashish K. Sahib
- Department of Neurology, Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, Los Angeles, CA, United States
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25
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Ren S, Guo K, Li Y, Cao YY, Wang ZQ, Tian Y. Diagnostic accuracy of apparent diffusion coefficient to differentiate intrapancreatic accessory spleen from pancreatic neuroendocrine tumors. World J Gastrointest Oncol 2023; 15:1051-1061. [PMID: 37389113 PMCID: PMC10302999 DOI: 10.4251/wjgo.v15.i6.1051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/01/2023] [Accepted: 04/19/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Intrapancreatic accessory spleen (IPAS) shares similar imaging findings with hypervascular pancreatic neuroendocrine tumors (PNETs), which may lead to unnecessary surgery.
AIM To investigate and compare the diagnostic performance of absolute apparent diffusion coefficient (ADC) and normalized ADC (lesion-to-spleen ADC ratios) in the differential diagnosis of IPAS from PNETs.
METHODS A retrospective study consisting of 29 patients (16 PNET patients vs 13 IPAS patients) who underwent preoperative contrast-enhanced magnetic resonance imaging together with diffusion-weighted imaging/ADC maps between January 2017 and July 2020 was performed. Two independent reviewers measured ADC on all lesions and spleens, and normalized ADC was calculated for further analysis. The receiver operating characteristics analysis was carried out for evaluating the diagnostic performance of both absolute ADC and normalized ADC values in the differential diagnosis between IPAS and PNETs by clarifying sensitivity, specificity, and accuracy. Inter-reader reliability for the two methods was evaluated.
RESULTS IPAS had a significantly lower absolute ADC (0.931 ± 0.773 × 10-3 mm2/s vs 1.254 ± 0.219 × 10-3 mm2/s) and normalized ADC value (1.154 ± 0.167 vs 1.591 ± 0.364) compared to PNET. A cutoff value of 1.046 × 10-3 mm2/s for absolute ADC was associated with 81.25% sensitivity, 100% specificity, and 89.66% accuracy with an area under the curve of 0.94 (95% confidence interval: 0.8536-1.000) for the differential diagnosis of IPAS from PNET. Similarly, a cutoff value of 1.342 for normalized ADC was associated with 81.25% sensitivity, 92.31% specificity, and 86.21% accuracy with an area under the curve of 0.91 (95% confidence interval: 0.8080-1.000) for the differential diagnosis of IPAS from PNET. Both methods showed excellent inter-reader reliability with intraclass correlation coefficients for absolute ADC and ADC ratio being 0.968 and 0.976, respectively.
CONCLUSION Both absolute ADC and normalized ADC values can facilitate the differentiation between IPAS and PNET.
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Affiliation(s)
- Shuai Ren
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Kai Guo
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Yuan Li
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Ying-Ying Cao
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Zhong-Qiu Wang
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
| | - Ying Tian
- Department of Radiology, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing 210029, Jiangsu Province, China
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Clemente A, Attyé A, Renard F, Calamante F, Burmester A, Imms P, Deutscher E, Akhlaghi H, Beech P, Wilson PH, Poudel G, Domínguez D JF, Caeyenberghs K. Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients. Brain Res 2023; 1806:148289. [PMID: 36813064 DOI: 10.1016/j.brainres.2023.148289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/22/2022] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
BACKGROUND AND PURPOSE Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. MATERIALS AND METHODS Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 - 64y). RESULTS Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted. CONCLUSIONS Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.
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Affiliation(s)
- Adam Clemente
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural, Health and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia.
| | - Arnaud Attyé
- CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France; School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Félix Renard
- CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia; Sydney Imaging - The University of Sydney, Sydney, Australia
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Australia
| | - Evelyn Deutscher
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Hamed Akhlaghi
- Emergency Department, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia; Department of Psychology, Faculty of Health, Deakin University, Australia
| | - Paul Beech
- Department of Radiology and Nuclear Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Peter H Wilson
- Development and Disability over the Lifespan Program, Healthy Brain and Mind Research Centre, School of Behavioural, Health and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Juan F Domínguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
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Wade RG, Lu F, Poruslrani Y, Karia C, Feltbower RG, Plein S, Bourke G, Teh I. Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb. Sci Rep 2023; 13:4852. [PMID: 36964186 PMCID: PMC10039047 DOI: 10.1038/s41598-023-31307-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
Peripheral neuropathy affects 1 in 10 adults over the age of 40 years. Given the absence of a reliable diagnostic test for peripheral neuropathy, there has been a surge of research into diffusion tensor imaging (DTI) because it characterises nerve microstructure and provides reproducible proxy measures of myelination, axon diameter, fibre density and organisation. Before researchers and clinicians can reliably use diffusion tensor imaging to assess the 'health' of the major nerves of the upper limb, we must understand the "normal" range of values and how they vary with experimental conditions. We searched PubMed, Embase, medRxiv and bioRxiv for studies which reported the findings of DTI of the upper limb in healthy adults. Four review authors independently triple extracted data. Using the meta suite of Stata 17, we estimated the normal fractional anisotropy (FA) and diffusivity (mean, MD; radial, RD; axial AD) values of the median, radial and ulnar nerve in the arm, elbow and forearm. Using meta-regression, we explored how DTI metrics varied with age and experimental conditions. We included 20 studies reporting data from 391 limbs, belonging to 346 adults (189 males and 154 females, ~ 1.2 M:1F) of mean age 34 years (median 31, range 20-80). In the arm, there was no difference in the FA (pooled mean 0.59 mm2/s [95% CI 0.57, 0.62]; I2 98%) or MD (pooled mean 1.13 × 10-3 mm2/s [95% CI 1.08, 1.18]; I2 99%) of the median, radial and ulnar nerves. Around the elbow, the ulnar nerve had a 12% lower FA than the median and radial nerves (95% CI - 0.25, 0.00) and significantly higher MD, RD and AD. In the forearm, the FA (pooled mean 0.55 [95% CI 0.59, 0.64]; I2 96%) and MD (pooled mean 1.03 × 10-3 mm2/s [95% CI 0.94, 1.12]; I2 99%) of the three nerves were similar. Multivariable meta regression showed that the b-value, TE, TR, spatial resolution and age of the subject were clinically important moderators of DTI parameters in peripheral nerves. We show that subject age, as well as the b-value, TE, TR and spatial resolution are important moderators of DTI metrics from healthy nerves in the adult upper limb. The normal ranges shown here may inform future clinical and research studies.
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Affiliation(s)
- Ryckie G Wade
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK.
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK.
| | - Fangqing Lu
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK
| | - Yohan Poruslrani
- Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Chiraag Karia
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | | | - Sven Plein
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Grainne Bourke
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Irvin Teh
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, UK
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Shima A, Sakai K, Yamashita F, Hamaguchi T, Kitamoto T, Sasaki M, Yamada M, Ono K. Vacuoles related to tissue neuron-astrocyte ratio and infiltration of macrophages/monocytes contribute to hyperintense brain signals on diffusion-weighted magnetic resonance imaging in sporadic Creutzfeldt-Jakob disease. J Neurol Sci 2023; 447:120612. [PMID: 36913815 DOI: 10.1016/j.jns.2023.120612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Revised: 03/02/2023] [Accepted: 03/02/2023] [Indexed: 03/08/2023]
Abstract
BACKGROUND Radiological features in patients with sporadic Creutzfeldt-Jakob disease (sCJD) are hyperintensity of the cerebral cortex and the basal ganglia displayed by diffusion-weighted magnetic resonance imaging (DW-MRI). We performed a quantitative study on neuropathological and radiological findings. METHODS Patient 1 received a definite diagnosis of MM1-type sCJD, while patient 2 received a definite diagnosis of MM1 + 2-type sCJD. Two DW-MRI scans were performed on each patient. DW-MRI was either taken the day before or on the day of the patient's death, and several hyperintense or isointense areas were marked as a region of interest (ROI). Mean signal intensity of the ROI was measured. Pathological quantitative assessments of the vacuoles, astrocytosis, infiltration of monocytes/macrophages, and proliferation of microglia was performed. Vacuole load (% area vacuole), glial fibrillary acidic protein (GFAP), CD68, and Iba-1 load were calculated. We defined spongiform change index (SCI) to indicate vacuoles related to a tissue neuron-astrocyte ratio. We assessed the correlation of intensity of the last DW-MRI and the pathological findings as well as association of changes of the signal intensity on the sequential images and the pathological findings. RESULT We observed a strong positive correlation between SCI and DW-MRI intensity. In the analysis using serial DW-MRI and pathological findings, we found that CD68 load was significantly larger in areas where signal intensity decreased, as compared to those areas where hyperintensity remained unchanged. CONCLUSION DW-MRI intensity in sCJD is associated with the ratio of neuron to astrocyte in the vacuoles and the infiltration of macrophages and/or monocytes.
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Affiliation(s)
- Ayano Shima
- Department of Neurology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takara-Machi, Kanazawa 920-8640, Japan
| | - Kenji Sakai
- Department of Neurology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takara-Machi, Kanazawa 920-8640, Japan; Department of Neurology, Joetsu General Hospital, 616 Daidofukuda, Joetsu, Niigata 943-8507, Japan.
| | - Fumio Yamashita
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate 028-3694, Japan
| | - Tsuyoshi Hamaguchi
- Department of Neurology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takara-Machi, Kanazawa 920-8640, Japan; Department of Neurology, Kanazawa Medical University, 1-1 Daigaku, Uchinada, Kahoku, Ishikawa 920-0293, Japan
| | - Tetsuyuki Kitamoto
- Department of Neurological Science, Tohoku University Graduate School of Medicine, 2-1 Seiyo-machi, Aoba-ku, Sendai 980-8565, Japan
| | - Makoto Sasaki
- Division of Ultrahigh Field MRI, Institute for Biomedical Sciences, Iwate Medical University, 1-1-1 Idaidori, Yahaba-cho, Shiwa-gun, Iwate 028-3694, Japan
| | - Masahito Yamada
- Department of Neurology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takara-Machi, Kanazawa 920-8640, Japan; Department of Internal Medicine, Division of Neurology, Kudanzaka Hospital, 1-6-12 Kudanzakaminami, Chiyoda-ku, Tokyo 102-0074, Japan
| | - Kenjiro Ono
- Department of Neurology, Kanazawa University Graduate School of Medical Sciences, 13-1 Takara-Machi, Kanazawa 920-8640, Japan.
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29
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Zanon Zotin MC, Yilmaz P, Sveikata L, Schoemaker D, van Veluw SJ, Etherton MR, Charidimou A, Greenberg SM, Duering M, Viswanathan A. Peak Width of Skeletonized Mean Diffusivity: A Neuroimaging Marker for White Matter Injury. Radiology 2023; 306:e212780. [PMID: 36692402 PMCID: PMC9968775 DOI: 10.1148/radiol.212780] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 10/01/2022] [Accepted: 10/14/2022] [Indexed: 01/25/2023]
Abstract
A leading cause of white matter (WM) injury in older individuals is cerebral small vessel disease (SVD). Cerebral SVD is the most prevalent vascular contributor to cognitive impairment and dementia. Therapeutic progress for cerebral SVD and other WM disorders depends on the development and validation of neuroimaging markers suitable as outcome measures in future interventional trials. Diffusion-tensor imaging (DTI) is one of the best-suited MRI techniques for assessing the extent of WM damage in the brain. But the optimal method to analyze individual DTI data remains hindered by labor-intensive and time-consuming processes. Peak width of skeletonized mean diffusivity (PSMD), a recently developed fast, fully automated DTI marker, was designed to quantify the WM damage secondary to cerebral SVD and reflect related cognitive impairment. Despite its promising results, knowledge about PSMD is still limited in the radiologic community. This focused review provides an overview of the technical details of PSMD while synthesizing the available data on its clinical and neuroimaging associations. From a critical expert viewpoint, the authors discuss the limitations of PSMD and its current validation status as a neuroimaging marker for vascular cognitive impairment. Finally, they point out the gaps to be addressed to further advance the field.
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Affiliation(s)
| | | | - Lukas Sveikata
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Dorothee Schoemaker
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Susanne J. van Veluw
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Mark R. Etherton
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Andreas Charidimou
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Steven M. Greenberg
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Marco Duering
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
| | - Anand Viswanathan
- From the J. Philip Kistler Stroke Research Center, Department of
Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Mass
(M.C.Z.Z., P.Y., L.S., D.S., S.J.v.V., M.R.E., A.C., S.M.G., A.V.); Center for
Imaging Sciences and Medical Physics, Department of Medical Imaging, Hematology
and Clinical Oncology, Ribeirão Preto Medical School, University of
São Paulo, 3900 Ten. Catão Roxo Street, Monte Alegre, Campus
Universitário, Ribeirão Preto, SP 14015-010, Brazil (M.C.Z.Z.);
Departments of Epidemiology and Radiology and Nuclear Medicine, Erasmus Medical
Center, Rotterdam, the Netherlands (P.Y.); Division of Neurology, Department of
Clinical Neurosciences, Geneva University Hospital, Faculty of Medicine,
University of Geneva, Geneva, Switzerland (L.S.); Institute of Cardiology,
Medical Academy, Lithuanian University of Health Sciences, Kaunas, Lithuania
(L.S.); and Medical Image Analysis Center (MIAC AG) and Quantitative Biomedical
Imaging Group (qbig), Department of Biomedical Engineering, University of Basel,
Basel, Switzerland (M.D.)
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30
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Stenberg J, Skandsen T, Gøran Moen K, Vik A, Eikenes L, Håberg AK. Diffusion Tensor and Kurtosis Imaging Findings the First Year following Mild Traumatic Brain Injury. J Neurotrauma 2023; 40:457-471. [PMID: 36305387 PMCID: PMC9986024 DOI: 10.1089/neu.2022.0206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Despite enormous research interest in diffusion tensor imaging and diffusion kurtosis imaging (DTI; DKI) following mild traumatic brain injury (MTBI), it remains unknown how diffusion in white matter evolves post-injury and relates to acute MTBI characteristics. This prospective cohort study aimed to characterize diffusion changes in white matter the first year after MTBI. Patients with MTBI (n = 193) and matched controls (n = 83) underwent 3T magnetic resonance imaging (MRI) within 72 h and 3- and 12-months post-injury. Diffusion data were analyzed in three steps: 1) voxel-wise comparisons between the MTBI and control group were performed with tract-based spatial statistics at each time-point; 2) clusters of significant voxels identified in step 1 above were evaluated longitudinally with mixed-effect models; 3) the MTBI group was divided into: (A) complicated (with macrostructural findings on MRI) and uncomplicated MTBI; (B) long (1-24 h) and short (< 1 h) post-traumatic amnesia (PTA); and (C) other and no other concurrent injuries to investigate if findings in step 1 were driven mainly by aberrant diffusion in patients with a more severe injury. At 72 h, voxel-wise comparisons revealed significantly lower fractional anisotropy (FA) in one tract and significantly lower mean kurtosis (Kmean) in 11 tracts in the MTBI compared with control group. At 3 months, the MTBI group had significantly higher mean diffusivity in eight tracts compared with controls. At 12 months, FA was significantly lower in four tracts and Kmean in 10 tracts in patients with MTBI compared with controls. There was considerable overlap in affected tracts across time, including the corpus callosum, corona radiata, internal and external capsule, and cerebellar peduncles. Longitudinal analyses revealed that the diffusion metrics remained relatively stable throughout the first year after MTBI. The significant group*time interactions identified were driven by changes in the control rather than the MTBI group. Further, differences identified in step 1 did not result from greater diffusion abnormalities in patients with complicated MTBI, long PTA, or other concurrent injuries, as standardized mean differences in diffusion metrics between the groups were small (0.07 ± 0.11) and non-significant. However, follow-up voxel-wise analyses revealed that other concurrent injuries had effects on diffusion metrics, but predominantly in other metrics and at other time-points than the effects observed in the MTBI versus control group analysis. In conclusion, patients with MTBI differed from controls in white matter integrity already 72 h after injury. Diffusion metrics remained relatively stable throughout the first year after MTBI and were not driven by deviating diffusion in patients with a more severe MTBI.
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Affiliation(s)
- Jonas Stenberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Toril Skandsen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Physical Medicine and Rehabilitation, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Kent Gøran Moen
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Radiology, Vestre Viken Hospital Trust, Drammen Hospital, Drammen, Norway.,Department of Radiology, Nord-Trøndelag Hospital Trust, Levanger Hospital, Levanger, Norway
| | - Anne Vik
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Neurosurgery, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
| | - Live Eikenes
- Department of Circulation and Medical Imaging, Norwegian University of Science and Technology (NTNU), Trondheim, Norway
| | - Asta K Håberg
- Department of Neuromedicine and Movement Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway.,Department of Radiology and Nuclear Medicine, St. Olavs Hospital, Trondheim University Hospital, Trondheim, Norway
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Chianca V, Albano D, Rizzo S, Maas M, Sconfienza LM, Del Grande F. Inter-vendor and inter-observer reliability of diffusion tensor imaging in the musculoskeletal system: a multiscanner MR study. Insights Imaging 2023; 14:32. [PMID: 36757529 PMCID: PMC9911574 DOI: 10.1186/s13244-023-01374-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2022] [Accepted: 01/09/2023] [Indexed: 02/10/2023] Open
Abstract
BACKGROUND To evaluate the inter-observer and inter-vendor reliability of diffusion tensor imaging parameters in the musculoskeletal system. METHODS This prospective study included six healthy volunteers three men (mean age: 42; range: 31-52 years) and three women (mean age: 36; range: 30-44 years). Each subject was scanned using different 3 Tesla magnetic resonance scanners from three different vendors at three different sites bilaterally. First, the intra-class correlation coefficient was used to determine between-observers agreement for overall measurements and clinical sites. Next, between-group comparisons were made through the nonparametric Friedman's test. Finally, the Bland-Altman method was used to determine agreement among the three scanner measurements, comparing them two by two. RESULTS A total of 792 measurement were calculated. ICC reported high levels of agreement between the two observers. ICC related to MD, FA, and RD measurements ranged from 0.88 (95% CI 0.85-0.90) to 0.95 (95% CI 0.94-0.96), from 0.85 (95% CI 0.81-0.88) to 0.95 (95% CI 0.93-0.96), and from 0.89 (0.85-0.90) to 0.92 (0.90-0.94). No statistically significant inter-vendor differences were observed. The Bland-Altmann method confirmed a high correlation between parameter values. CONCLUSION An excellent inter-observer and inter-vendor reliability was found in our study.
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Affiliation(s)
- Vito Chianca
- Clinica di Radiologia EOC IIMSI, Lugano, Switzerland. .,Ospedale Evangelico Betania, Via Argine 604, 80147, Naples, Italy.
| | - Domenico Albano
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milan, Italy
| | | | - Mario Maas
- grid.7177.60000000084992262Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centres, University of Amsterdam, Amsterdam, The Netherlands ,Amsterdam Movement Sciences Research Institute, Amsterdam, The Netherlands
| | - Luca Maria Sconfienza
- grid.417776.4IRCCS Istituto Ortopedico Galeazzi, Milan, Italy ,grid.4708.b0000 0004 1757 2822Department of Biomedical Sciences for Health, University of Milano, Milan, Italy
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Kirschen MP, Berman JI, Liu H, Ouyang M, Mondal A, Griffis H, Levow C, Winters M, Lang SS, Huh J, Huang H, Berg RA, Vossough A, Topjian A. Association Between Quantitative Diffusion-Weighted Magnetic Resonance Neuroimaging and Outcome After Pediatric Cardiac Arrest. Neurology 2022; 99:e2615-e2626. [PMID: 36028319 PMCID: PMC9754647 DOI: 10.1212/wnl.0000000000201189] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 07/15/2022] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Diffusion MRI can quantify the extent of hypoxic-ischemic brain injury after cardiac arrest. Our objective was to determine the association between the adult-derived threshold of apparent diffusion coefficient (ADC) <650 × 10-6 mm2/s in >10% of brain tissue and an unfavorable outcome after pediatric cardiac arrest. Since ADC decreases exponentially as a function of increasing age, we determined the association between (1) having >10% of brain tissue below a novel age-dependent ADC threshold, and (2) age-normalized whole-brain mean ADC and unfavorable outcome. METHODS This was a retrospective study of patients aged ≤18 years who had cardiac arrest and a clinically obtained brain MRI within 7 days. The primary outcome was unfavorable neurologic status at hospital discharge based on the Pediatric Cerebral Performance Category score. ADC images were extracted from 3-direction diffusion imaging. We determined whether each patient had >10% of voxels with an ADC below prespecified thresholds. We computed the whole-brain mean ADC for each patient. RESULTS One hundred thirty-four patients were analyzed. Patients with ADC <650 × 10-6 mm2/s in >10% of voxels had 15 times higher odds (95% CI 5-65) of an unfavorable outcome compared with patients with ADC <650 × 10-6 mm2/s (area under the receiver operating characteristic curve [AUROC] 0.72 [95% CI 0.63-0.80]). These ADC criteria had a sensitivity and specificity of 0.49 and 0.94, respectively, and positive and negative predictive values of 0.93 and 0.52, respectively, for an unfavorable outcome. The age-dependent ADC threshold that yielded optimal sensitivity and specificity for unfavorable outcomes was <300 × 10-6 mm2/s below each patient's predicted whole-brain mean ADC. The sensitivity, specificity, and positive and negative predictive values for this ADC threshold were 0.53, 0.96, 0.96, and 0.54, respectively (odds ratio [OR] 26.4 [95% CI 7.5-168.3]; AUROC 0.74 [95% CI 0.66-0.83]). Lower age-normalized whole-brain mean ADC was also associated with an unfavorable outcome (OR 0.42 [0.24-0.64], AUROC 0.76 [95% CI 0.66-0.82]). DISCUSSION Quantitative diffusion thresholds on MRI within 7 days after cardiac arrest were associated with an unfavorable outcome in children. The age-independent ADC threshold was highly specific for predicting an unfavorable outcome. However, the specificity and sensitivity increased when using age-dependent ADC thresholds. Age-dependent ADC thresholds may improve prognostic accuracy and require further investigation in larger cohorts. CLASSIFICATION OF EVIDENCE This study provides Class III evidence that quantitative diffusion-weighted imaging within 7 days postarrest can predict an unfavorable clinical outcome in children.
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Affiliation(s)
- Matthew P Kirschen
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia.
| | - Jeffrey I Berman
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Hongyan Liu
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Minhui Ouyang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Antara Mondal
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Heather Griffis
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Cindee Levow
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Madeline Winters
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Shih-Shan Lang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Jimmy Huh
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Hao Huang
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Robert A Berg
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Arastoo Vossough
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
| | - Alexis Topjian
- From the Departments of Anesthesiology and Critical Care Medicine (M.P.K., C.L., M.W., J.H., R.A.B., A.T.), and Radiology (J.I.B., M.O., H.H., A.V.); Data Science and Biostatistics Unit (H.L., A.M., H.G.), Department of Biomedical and Health Informatics, and Department of Neurosurgery (S.-S.L.), Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia
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Richter S, Winzeck S, Correia MM, Kornaropoulos EN, Manktelow A, Outtrim J, Chatfield D, Posti JP, Tenovuo O, Williams GB, Menon DK, Newcombe VF. Validation of cross-sectional and longitudinal ComBat harmonization methods for magnetic resonance imaging data on a travelling subject cohort. NEUROIMAGE. REPORTS 2022; 2:None. [PMID: 36507071 PMCID: PMC9726680 DOI: 10.1016/j.ynirp.2022.100136] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/25/2022] [Revised: 09/07/2022] [Accepted: 09/14/2022] [Indexed: 11/05/2022]
Abstract
Background The growth in multi-center neuroimaging studies generated a need for methods that mitigate the differences in hardware and acquisition protocols across sites i.e., scanner effects. ComBat harmonization methods have shown promise but have not yet been tested on all the data types commonly studied with magnetic resonance imaging (MRI). This study aimed to validate neuroCombat, longCombat and gamCombat on both structural and diffusion metrics in both cross-sectional and longitudinal data. Methods We used a travelling subject design whereby 73 healthy volunteers contributed 161 scans across two sites and four machines using one T1 and five diffusion MRI protocols. Scanner was defined as a composite of site, machine and protocol. A common pipeline extracted two structural metrics (volumes and cortical thickness) and two diffusion tensor imaging metrics (mean diffusivity and fractional anisotropy) for seven regions of interest including gray and (except for cortical thickness) white matter regions. Results Structural data exhibited no significant scanner effect and therefore did not benefit from harmonization in our particular cohort. Indeed, attempting harmonization obscured the true biological effect for some regions of interest. Diffusion data contained marked scanner effects and was successfully harmonized by all methods, resulting in smaller scanner effects and better detection of true biological effects. LongCombat less effectively reduced the scanner effect for cross-sectional white matter data but had a slightly lower probability of incorrectly finding group differences in simulations, compared to neuroCombat and gamCombat. False positive rates for all methods and all metrics did not significantly exceed 5%. Conclusions Statistical harmonization of structural data is not always necessary and harmonization in the absence of a scanner effect may be harmful. Harmonization of diffusion MRI data is highly recommended with neuroCombat, longCombat and gamCombat performing well in cross-sectional and longitudinal settings.
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Affiliation(s)
- Sophie Richter
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- Corresponding author.
| | - Stefan Winzeck
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
- BioMedIA Group, Department of Computing, Imperial College London, London, UK
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | | | - Anne Manktelow
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Joanne Outtrim
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Doris Chatfield
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
| | - Jussi P. Posti
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
- Department of Neurosurgery, Turku University Hospital, Turku, Finland
| | - Olli Tenovuo
- Turku Brain Injury Center, Turku University Hospital & University of Turku, Turku, Finland
| | - Guy B. Williams
- Wolfson Brain Imaging Centre, Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - David K. Menon
- Division of Anaesthesia, Department of Medicine, University of Cambridge, Cambridge, UK
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Paquier Z, Chao SL, Bregni G, Sanchez AV, Guiot T, Dhont J, Gulyban A, Levillain H, Sclafani F, Reynaert N, Bali MA. Pre-trial quality assurance of diffusion-weighted MRI for radiomic analysis and the role of harmonisation. Phys Med 2022; 103:138-146. [DOI: 10.1016/j.ejmp.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 09/30/2022] [Accepted: 10/08/2022] [Indexed: 11/17/2022] Open
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Stulík J, Keřkovský M, Kuhn M, Svobodová M, Benešová Y, Bednařík J, Šprláková-Puková A, Mechl M, Dostál M. Evaluating Magnetic Resonance Diffusion Properties Together with Brain Volumetry May Predict Progression to Multiple Sclerosis. Acad Radiol 2022; 29:1493-1501. [PMID: 35067451 DOI: 10.1016/j.acra.2021.12.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Revised: 11/29/2021] [Accepted: 12/11/2021] [Indexed: 12/14/2022]
Abstract
RATIONALE AND OBJECTIVES Although the gold standard in predicting future progression from clinically isolated syndrome (CIS) to clinically definite multiple sclerosis (CDMS) consists in the McDonald criteria, efforts are being made to employ various advanced MRI techniques for predicting clinical progression. This study's main aim was to evaluate the predictive power of diffusion tensor imaging (DTI) of the brain and brain volumetry to distinguish between patients having CIS with future progression to CDMS from those without progression during the following 2 years and to compare those parameters with conventional MRI evaluation. MATERIALS AND METHODS All participants underwent an MRI scan of the brain. DTI and volumetric data were processed and various parameters were compared between the study groups. RESULTS We found significant differences between the subgroups of patients differing by future progression to CDMS in most of those DTI and volumetric parameters measured. Fractional anisotropy of water diffusion proved to be the strongest predictor of clinical conversion among all parameters evaluated, demonstrating also higher specificity compared to evaluation of conventional MRI images according to McDonald criteria. CONCLUSION Conclusion: Our results provide evidence that the evaluation of DTI parameters together with brain volumetry in patients with early-stage CIS may be useful in predicting conversion to CDMS within the following 2 years of the disease course.
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Affiliation(s)
- Jakub Stulík
- Department of Radiology and Nuclear Medicine, University Hospital Brno, Jihlavská 20 Brno, 62500, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Miloš Keřkovský
- Department of Radiology and Nuclear Medicine, University Hospital Brno, Jihlavská 20 Brno, 62500, Czech Republic; Institute of Biostatistics and Analyses, Masaryk University, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic.
| | - Matyáš Kuhn
- Department of Psychiatry, University Hospital Brno, Brno, Czech Republic; Behavioural and Social Neuroscience, CEITEC Masaryk University, Brno, Czech Republic
| | - Monika Svobodová
- Department of Neurology, University Hospital Brno, Brno, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Yvonne Benešová
- Department of Neurology, University Hospital Brno, Brno, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Josef Bednařík
- Department of Neurology, University Hospital Brno, Brno, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Andrea Šprláková-Puková
- Department of Radiology and Nuclear Medicine, University Hospital Brno, Jihlavská 20 Brno, 62500, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marek Mechl
- Department of Radiology and Nuclear Medicine, University Hospital Brno, Jihlavská 20 Brno, 62500, Czech Republic; Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Marek Dostál
- Department of Radiology and Nuclear Medicine, University Hospital Brno, Jihlavská 20 Brno, 62500, Czech Republic; Department of Biophysics, Masaryk University, Brno, Czech Republic
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Lee W, Choi G, Lee J, Park H. Registration and quantification network (RQnet) for IVIM-DKI analysis in MRI. Magn Reson Med 2022; 89:250-261. [PMID: 36121205 DOI: 10.1002/mrm.29454] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2022] [Revised: 08/22/2022] [Accepted: 08/22/2022] [Indexed: 11/08/2022]
Abstract
PURPOSE A deep learning method is proposed for aligning diffusion weighted images (DWIs) and estimating intravoxel incoherent motion-diffusion kurtosis imaging parameters simultaneously. METHODS We propose an unsupervised deep learning method that performs 2 tasks: registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. A common registration method in diffusion MRI is based on minimizing dissimilarity between various DWIs, which may result in registration errors due to different contrasts in different DWIs. We designed a novel unsupervised deep learning method for both accurate registration and quantification of various diffusion parameters. In order to generate motion-simulated training data and test data, 17 volunteers were scanned without moving their heads, and 4 volunteers moved their heads during the scan in a 3 Tesla MRI. In order to investigate the applicability of the proposed method to other organs, kidney images were also obtained. We compared the registration accuracy of the proposed method, statistical parametric mapping, and a deep learning method with a normalized cross-correlation loss. In the quantification part of the proposed method, a deep learning method that considered the diffusion gradient direction was used. RESULTS Simulations and experimental results showed that the proposed method accurately performed registration and quantification for intravoxel incoherent motion-diffusion kurtosis imaging analysis. The registration accuracy of the proposed method was high for all b values. Furthermore, quantification performance was analyzed through simulations and in vivo experiments, where the proposed method showed the best performance among the compared methods. CONCLUSION The proposed method aligns the DWIs and accurately quantifies the intravoxel incoherent motion-diffusion kurtosis imaging parameters.
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Affiliation(s)
- Wonil Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Giyong Choi
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Jongyeon Lee
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - HyunWook Park
- Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
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Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems. Neuroimage 2022; 257:119290. [PMID: 35545197 PMCID: PMC9248353 DOI: 10.1016/j.neuroimage.2022.119290] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/06/2022] [Accepted: 05/06/2022] [Indexed: 12/13/2022] Open
Abstract
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
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Vlegels N, Ossenkoppele R, van der Flier WM, Koek HL, Reijmer YD, Wisse LEM, Biessels GJ. Does Loss of Integrity of the Cingulum Bundle Link Amyloid-β Accumulation and Neurodegeneration in Alzheimer’s Disease? J Alzheimers Dis 2022; 89:39-49. [DOI: 10.3233/jad-220024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
Background: Alzheimer’s disease is characterized by the accumulation of amyloid-β (Aβ) into plaques, aggregation of tau into neurofibrillary tangles, and neurodegenerative processes including atrophy. However, there is a poorly understood spatial discordance between initial Aβ deposition and local neurodegeneration. Objective: Here, we test the hypothesis that the cingulum bundle links Aβ deposition in the cingulate cortex to medial temporal lobe (MTL) atrophy. Methods: 21 participants with mild cognitive impairment (MCI) from the UMC Utrecht memory clinic (UMCU, discovery sample) and 37 participants with MCI from Alzheimer’s Disease Neuroimaging Initiative (ADNI, replication sample) with available Aβ-PET scan, T1-weighted and diffusion-weighted MRI were included. Aβ load of the cingulate cortex was measured by the standardized uptake value ratio (SUVR), white matter integrity of the cingulum bundle was assessed by mean diffusivity and atrophy of the MTL by normalized MTL volume. Relationships were tested with linear mixed models, to accommodate multiple measures for each participant. Results: We found at most a weak association between cingulate Aβ and MTL volume (added R2 <0.06), primarily for the posterior hippocampus. In neither sample, white matter integrity of the cingulum bundle was associated with cingulate Aβ or MTL volume (added R2 <0.01). Various sensitivity analyses (Aβ-positive individuals only, posterior cingulate SUVR, MTL sub region volume) provided similar results. Conclusion: These findings, consistent in two independent cohorts, do not support our hypothesis that loss of white matter integrity of the cingulum is a connecting factor between cingulate gyrus Aβ deposition and MTL atrophy.
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Affiliation(s)
- Naomi Vlegels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Rik Ossenkoppele
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Clinical Memory Research Unit, Lund University, Lund, Sweden
| | - Wiesje M. van der Flier
- Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam UMC, Amsterdam, The Netherlands
- Department of Epidemiology and Data Science, VU University Medical Center, Amsterdam, The Netherlands
| | - Huiberdina L. Koek
- Department of Geriatrics, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yael D. Reijmer
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Laura EM Wisse
- Department of Diagnostic Radiology, Lund University, Lund, Sweden
| | - Geert Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
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Santoro JD, Moon PK, Han M, McKenna ES, Tong E, MacEachern SJ, Forkert ND, Yeom KW. Early Onset Diffusion Abnormalities in Refractory Headache Disorders. Front Neurol 2022; 13:898219. [PMID: 35775057 PMCID: PMC9237368 DOI: 10.3389/fneur.2022.898219] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/16/2022] [Indexed: 11/30/2022] Open
Abstract
Objective This study sought to determine if individuals with medically refractory migraine headache have volume or diffusion abnormalities on neuroimaging compared to neurotypical individuals. Background Neuroimaging biomarkers in headache medicine continue to be limited. Early prediction of medically refractory headache and migraine disorders could result in earlier administration of high efficacy therapeutics. Methods A single-center, retrospective, case control study was performed. All patients were evaluated clinically between 2014 and 2018. Individuals with medically refractory migraine headache (defined by ICDH-3 criteria) without any other chronic medical diseases were enrolled. Patients had to have failed more than two therapeutics and aura was not exclusionary. The initial MRI study for each patient was reviewed. Multiple brain regions were analyzed for volume and apparent diffusion coefficient values. These were compared to 81 neurotypical control patients. Results A total of 79 patients with medically refractory migraine headache were included and compared to 74 neurotypical controls without headache disorders. Time between clinical diagnosis and neuroimaging was a median of 24 months (IQR: 12.0–37.0). Comparison of individuals with medically refractory migraine headache to controls revealed statistically significant differences in median apparent diffusion coefficient (ADC) in multiple brain subregions (p < 0.001). Post-hoc pair-wise analysis comparing individuals with medically refractory migraine headache to control patients revealed significantly decreased median ADC values for the thalamus, caudate, putamen, pallidum, amygdala, brainstem, and cerebral white matter. No volumetric differences were observed between groups. Conclusions In individuals with medically refractory MH, ADC changes are measurable in multiple brain structures at an early age, prior to the failure of multiple pharmacologic interventions and the diagnosis of medically refractory MH. This data supports the hypothesis that structural connectivity issues may predispose some patients toward more medically refractory pain disorders such as MH.
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Affiliation(s)
- Jonathan D. Santoro
- Division of Neurology, Department of Pediatrics, Children's Hospital Los Angeles, Los Angeles, CA, United States
- Department of Neurology, Keck School of Medicine at University of Southern California, Los Angeles, CA, United States
- *Correspondence: Jonathan D. Santoro
| | - Peter K. Moon
- Stanford University School of Medicine, Stanford, CA, United States
| | - Michelle Han
- Department of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, United States
| | - Emily S. McKenna
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | - Elizabeth Tong
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
| | | | - Nils D. Forkert
- Department of Radiology, University of Calgary, Calgary, AB, Canada
| | - Kristen W. Yeom
- Department of Radiology, Stanford University School of Medicine, Stanford, CA, United States
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Teh I, Romero R. WA, Boyle J, Coll‐Font J, Dall'Armellina E, Ennis DB, Ferreira PF, Kalra P, Kolipaka A, Kozerke S, Lohr D, Mongeon F, Moulin K, Nguyen C, Nielles‐Vallespin S, Raterman B, Schreiber LM, Scott AD, Sosnovik DE, Stoeck CT, Tous C, Tunnicliffe EM, Weng AM, Croisille P, Viallon M, Schneider JE. Validation of cardiac diffusion tensor imaging sequences: A multicentre test-retest phantom study. NMR IN BIOMEDICINE 2022; 35:e4685. [PMID: 34967060 PMCID: PMC9285553 DOI: 10.1002/nbm.4685] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 11/19/2021] [Accepted: 12/24/2021] [Indexed: 05/23/2023]
Abstract
Cardiac diffusion tensor imaging (DTI) is an emerging technique for the in vivo characterisation of myocardial microstructure, and there is a growing need for its validation and standardisation. We sought to establish the accuracy, precision, repeatability and reproducibility of state-of-the-art pulse sequences for cardiac DTI among 10 centres internationally. Phantoms comprising 0%-20% polyvinylpyrrolidone (PVP) were scanned with DTI using a product pulsed gradient spin echo (PGSE; N = 10 sites) sequence, and a custom motion-compensated spin echo (SE; N = 5) or stimulated echo acquisition mode (STEAM; N = 5) sequence suitable for cardiac DTI in vivo. A second identical scan was performed 1-9 days later, and the data were analysed centrally. The average mean diffusivities (MDs) in 0% PVP were (1.124, 1.130, 1.113) x 10-3 mm2 /s for PGSE, SE and STEAM, respectively, and accurate to within 1.5% of reference data from the literature. The coefficients of variation in MDs across sites were 2.6%, 3.1% and 2.1% for PGSE, SE and STEAM, respectively, and were similar to previous studies using only PGSE. Reproducibility in MD was excellent, with mean differences in PGSE, SE and STEAM of (0.3 ± 2.3, 0.24 ± 0.95, 0.52 ± 0.58) x 10-5 mm2 /s (mean ± 1.96 SD). We show that custom sequences for cardiac DTI provide accurate, precise, repeatable and reproducible measurements. Further work in anisotropic and/or deforming phantoms is warranted.
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Affiliation(s)
- Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - William A. Romero R.
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Jordan Boyle
- School of Mechanical EngineeringUniversity of LeedsLeedsUK
| | - Jaume Coll‐Font
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Erica Dall'Armellina
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
| | - Daniel B. Ennis
- Division of RadiologyVA Palo Alto Health Care SystemPalo AltoCaliforniaUSA
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Pedro F. Ferreira
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Prateek Kalra
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Arunark Kolipaka
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Sebastian Kozerke
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - David Lohr
- Department of Cardiovascular ImagingComprehensive Heart Failure CenterWürzburgGermany
| | | | - Kévin Moulin
- Department of RadiologyStanford UniversityStanfordCaliforniaUSA
| | - Christopher Nguyen
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Sonia Nielles‐Vallespin
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - Brian Raterman
- Department of RadiologyThe Ohio State University Wexner Medical CenterColumbusOhioUSA
| | - Laura M. Schreiber
- Department of Cardiovascular ImagingComprehensive Heart Failure CenterWürzburgGermany
| | - Andrew D. Scott
- Cardiovascular Magnetic Resonance UnitThe Royal Brompton and Harefield NHS Foundation TrustLondonUK
- National Heart and Lung InstituteImperial College LondonLondonUK
| | - David E. Sosnovik
- Cardiovascular Research Center and A. A. Martinos Center for Biomedical ImagingMassachusetts General Hospital and Harvard Medical SchoolBostonMassachusettsUSA
| | - Christian T. Stoeck
- Institute for Biomedical EngineeringUniversity and ETH ZurichZurichSwitzerland
| | - Cyril Tous
- Department of Radiology, Radiation‐Oncology and Nuclear Medicine and Institute of Biomedical EngineeringUniversité de MontréalMontréalCanada
| | - Elizabeth M. Tunnicliffe
- Radcliffe Department of MedicineUniversity of OxfordOxfordUK
- Oxford NIHR Biomedical Research CentreOxfordUK
| | - Andreas M. Weng
- Department of Diagnostic and Interventional RadiologyUniversity Hospital WürzburgWürzburgGermany
| | - Pierre Croisille
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Magalie Viallon
- Univ Lyon, INSA‐Lyon, Université Claude Bernard Lyon 1UJM‐Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1294, F‐42023Saint EtienneFrance
| | - Jürgen E. Schneider
- Leeds Institute of Cardiovascular and Metabolic MedicineUniversity of LeedsLeedsUK
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Prognosis After Cardiac Arrest: The Additional Value of DWI and FLAIR to EEG. Neurocrit Care 2022; 37:302-313. [DOI: 10.1007/s12028-022-01498-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 03/28/2022] [Indexed: 10/18/2022]
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Keenan KE, Delfino JG, Jordanova KV, Poorman ME, Chirra P, Chaudhari AS, Baessler B, Winfield J, Viswanath SE, deSouza NM. Challenges in ensuring the generalizability of image quantitation methods for MRI. Med Phys 2022; 49:2820-2835. [PMID: 34455593 PMCID: PMC8882689 DOI: 10.1002/mp.15195] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 08/17/2021] [Accepted: 08/17/2021] [Indexed: 01/31/2023] Open
Abstract
Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
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Affiliation(s)
- Kathryn E. Keenan
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Jana G. Delfino
- Center for Devices and Radiological Health, US Food and Drug Administration, 10993 New Hampshire Ave, Silver Spring, MD 20993, USA
| | - Kalina V. Jordanova
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Megan E. Poorman
- Physical Measurement Laboratory, National Institute of Standards and Technology, 325 Broadway, Boulder, CO 80305, USA
| | - Prathyush Chirra
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Akshay S. Chaudhari
- Department of Radiology, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
- Department of Biomedical Data Science, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
| | - Bettina Baessler
- University Hospital of Zurich and University of Zurich, Raemistrasse 100, 8091 Zurich, Switzerland
| | - Jessica Winfield
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
| | - Satish E. Viswanath
- Dept of Biomedical Engineering, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, USA
| | - Nandita M. deSouza
- Cancer Research UK Cancer Imaging Centre, Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London, SW7 3RP, UK
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey, SM2 5PT, UK
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Ram Kim B, Kang Y, Lee J, Choi D, Joon Lee K, Mo Ahn J, Lee E, Woo Lee J, Sik Kang H. Tumor grading of soft tissue sarcomas: assessment with whole-tumor histogram analysis of apparent diffusion coefficient. Eur J Radiol 2022; 151:110319. [DOI: 10.1016/j.ejrad.2022.110319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2021] [Revised: 04/09/2022] [Accepted: 04/12/2022] [Indexed: 11/28/2022]
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Snider SB, Fischer D, McKeown ME, Cohen AL, Schaper FLWVJ, Amorim E, Fox MD, Scirica B, Bevers MB, Lee JW. Regional Distribution of Brain Injury After Cardiac Arrest: Clinical and Electrographic Correlates. Neurology 2022; 98:e1238-e1247. [PMID: 35017304 PMCID: PMC8967331 DOI: 10.1212/wnl.0000000000013301] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2021] [Accepted: 12/27/2021] [Indexed: 11/15/2022] Open
Abstract
BACKGROUND AND OBJECTIVES Disorders of consciousness, EEG background suppression, and epileptic seizures are associated with poor outcome after cardiac arrest. Our objective was to identify the distribution of diffusion MRI-measured anoxic brain injury after cardiac arrest and to define the regional correlates of disorders of consciousness, EEG background suppression, and seizures. METHODS We analyzed patients from a single-center database of unresponsive patients who underwent diffusion MRI after cardiac arrest (n = 204). We classified each patient according to recovery of consciousness (command following) before discharge, the most continuous EEG background (burst suppression vs continuous), and the presence or absence of seizures. Anoxic brain injury was measured with the apparent diffusion coefficient (ADC) signal. We identified ADC abnormalities relative to controls without cardiac arrest (n = 48) and used voxel lesion symptom mapping to identify regional associations with disorders of consciousness, EEG background suppression, and seizures. We then used a bootstrapped lasso regression procedure to identify robust, multivariate regional associations with each outcome variable. Last, using area under receiver operating characteristic curves, we then compared the classification ability of the strongest regional associations to that of brain-wide summary measures. RESULTS Compared to controls, patients with cardiac arrest demonstrated ADC signal reduction that was most significant in the occipital lobes. Disorders of consciousness were associated with reduced ADC most prominently in the occipital lobes but also in deep structures. Regional injury more accurately classified patients with disorders of consciousness than whole-brain injury. Background suppression mapped to a similar set of brain regions, but regional injury could no better classify patients than whole-brain measures. Seizures were less common in patients with more severe anoxic injury, particularly in those with injury to the lateral temporal white matter. DISCUSSION Anoxic brain injury was most prevalent in posterior cerebral regions, and this regional pattern of injury was a better predictor of disorders of consciousness than whole-brain injury measures. EEG background suppression lacked a specific regional association, but patients with injury to the temporal lobe were less likely to have seizures. Regional patterns of anoxic brain injury are relevant to the clinical and electrographic sequelae of cardiac arrest and may hold importance for prognosis. CLASSIFICATION OF EVIDENCE This study provides Class IV evidence that disorders of consciousness after cardiac arrest are associated with widely lower ADC values on diffusion MRI and are most strongly associated with reductions in occipital ADC.
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Affiliation(s)
- Samuel B Snider
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA.
| | - David Fischer
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Morgan E McKeown
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Alexander Li Cohen
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Frederic L W V J Schaper
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Edilberto Amorim
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Michael D Fox
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Benjamin Scirica
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Matthew B Bevers
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
| | - Jong Woo Lee
- From the Division of Neurocritical Care, Department of Neurology, (S.B.S., D.F., M.E.M., M.B.B.), Departments of Neurology, Psychiatry, and Radiology (A.L.C., F.L.W.V.J.S., M.D.F.), Center for Brain Circuit Therapeutics, Division of Cardiology, Department of Medicine (B.S.), and Division of Epilepsy, Department of Neurology (J.W.L.), Brigham and Women's Hospital, Harvard Medical School; Departments of Neurology and Radiology (A.L.C.), Computational Radiology Laboratory, Boston Children's Hospital, Harvard Medical School, MA; Department of Neurology (E.A.), Weill Institute for Neurosciences, University of California at San Francisco; Neurology Service (E.A.), Zuckerberg San Francisco General Hospital, CA; Departments of Neurology and Radiology (M.D.F.), Athinoula A. Martinos Centre for Biomedical Imaging, Massachusetts General Hospital, Charlestown; and Department of Neurology (M.D.F.), Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA
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Predictive MRI Biomarkers in MS—A Critical Review. Medicina (B Aires) 2022; 58:medicina58030377. [PMID: 35334554 PMCID: PMC8949449 DOI: 10.3390/medicina58030377] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 02/12/2022] [Accepted: 02/21/2022] [Indexed: 11/16/2022] Open
Abstract
Background and Objectives: In this critical review, we explore the potential use of MRI measurements as prognostic biomarkers in multiple sclerosis (MS) patients, for both conventional measurements and more novel techniques such as magnetization transfer, diffusion tensor, and proton spectroscopy MRI. Materials and Methods: All authors individually and comprehensively reviewed each of the aspects listed below in PubMed, Medline, and Google Scholar. Results: There are numerous MRI metrics that have been proven by clinical studies to hold important prognostic value for MS patients, most of which can be readily obtained from standard 1.5T MRI scans. Conclusions: While some of these parameters have passed the test of time and seem to be associated with a reliable predictive power, some are still better interpreted with caution. We hope this will serve as a reminder of how vast a resource we have on our hands in this versatile tool—it is up to us to make use of it.
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Initial Experience on Hyperpolarized [1-13C]Pyruvate MRI Multicenter Reproducibility—Are Multicenter Trials Feasible? Tomography 2022; 8:585-595. [PMID: 35314625 PMCID: PMC8938827 DOI: 10.3390/tomography8020048] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 12/31/2022] Open
Abstract
Background: Magnetic resonance imaging (MRI) with hyperpolarized [1-13C]pyruvate allows real-time and pathway specific clinical detection of otherwise unimageable in vivo metabolism. However, the comparability between sites and protocols is unknown. Here, we provide initial experiences on the agreement of hyperpolarized MRI between sites and protocols by repeated imaging of same healthy volunteers in Europe and the US. Methods: Three healthy volunteers traveled for repeated multicenter brain MRI exams with hyperpolarized [1-13C]pyruvate within one year. First, multisite agreement was assessed with the same echo-planar imaging protocol at both sites. Then, this was compared to a variable resolution echo-planar imaging protocol. In total, 12 examinations were performed. Common metrics of 13C-pyruvate to 13C-lactate conversion were calculated, including the kPL, a model-based kinetic rate constant, and its model-free equivalents. Repeatability was evaluated with intraclass correlation coefficients (ICC) for absolute agreement computed using two-way random effects models. Results: The mean kPL across all examinations in the multisite comparison was 0.024 ± 0.0016 s−1. The ICC of the kPL was 0.83 (p = 0.14) between sites and 0.7 (p = 0.09) between examinations of the same volunteer at any of the two sites. For the model-free metrics, the lactate Z-score had similar site-to-site ICC, while it was considerably lower for the lactate-to-pyruvate ratio. Conclusions: Estimation of metabolic conversion from hyperpolarized [1-13C]pyruvate to lactate using model-based metrics such as kPL suggests close agreement between sites and examinations in volunteers. Our initial results support harmonization of protocols, support multicenter studies, and inform their design.
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Taoka T, Ito R, Nakamichi R, Kamagata K, Sakai M, Kawai H, Nakane T, Abe T, Ichikawa K, Kikuta J, Aoki S, Naganawa S. Reproducibility of diffusion tensor image analysis along the perivascular space (DTI-ALPS) for evaluating interstitial fluid diffusivity and glymphatic function: CHanges in Alps index on Multiple conditiON acquIsition eXperiment (CHAMONIX) study. Jpn J Radiol 2022; 40:147-158. [PMID: 34390452 PMCID: PMC8803717 DOI: 10.1007/s11604-021-01187-5] [Citation(s) in RCA: 99] [Impact Index Per Article: 49.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Accepted: 07/29/2021] [Indexed: 02/08/2023]
Abstract
PURPOSE The diffusion tensor image analysis along the perivascular space (DTI-ALPS) method was developed to evaluate the brain's glymphatic function or interstitial fluid dynamics. This study aimed to evaluate the reproducibility of the DTI-ALPS method and the effect of modifications in the imaging method and data evaluation. MATERIALS AND METHODS Seven healthy volunteers were enrolled in this study. Image acquisition was performed for this test-retest study using a fixed imaging sequence and modified imaging methods which included the placement of region of interest (ROI), imaging plane, head position, averaging, number of motion-proving gradients, echo time (TE), and a different scanner. The ALPS-index values were evaluated for the change of conditions listed above. RESULTS This test-retest study by a fixed imaging sequence showed very high reproducibility (intraclass coefficient = 0.828) for the ALPS-index value. The bilateral ROI placement showed higher reproducibility. The number of averaging and the difference of the scanner did not influence the ALPS-index values. However, modification of the imaging plane and head position impaired reproducibility, and the number of motion-proving gradients affected the ALPS-index value. The ALPS-index values from 12-axis DTI and 3-axis diffusion-weighted image (DWI) showed good correlation (r = 0.86). Also, a shorter TE resulted in a larger value of the ALPS-index. CONCLUSION ALPS index was robust under the fixed imaging method even when different scanners were used. ALPS index was influenced by the imaging plane, the number of motion-proving gradient axes, and TE in the imaging sequence. These factors should be uniformed in the planning ALPS method studies. The possibility to develop a 3-axis DWI-ALPS method using three axes of the motion-proving gradient was also suggested.
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Affiliation(s)
- Toshiaki Taoka
- Department of Innovative Biomedical Visualization (iBMV), Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan.
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan.
| | - Rintaro Ito
- Department of Innovative Biomedical Visualization (iBMV), Nagoya University, 65 Tsurumai-cho, Showa-ku, Nagoya, Aichi, 466-8550, Japan
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan
| | - Rei Nakamichi
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Mayuko Sakai
- Canon Medical Systems Corporation, Otawara, Japan
| | - Hisashi Kawai
- Department of Radiology, Aichi Medical University, Nagakute, Japan
| | - Toshiki Nakane
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan
| | - Takashi Abe
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan
| | - Kazushige Ichikawa
- Department of Radiological Technology, Nagoya University Hospital, Nagoya, Aichi, Japan
| | - Junko Kikuta
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University, Nagoya, Aichi, Japan
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Alisch JSR, Egan JM, Bouhrara M. Differences in the choroid plexus volume and microstructure are associated with body adiposity. Front Endocrinol (Lausanne) 2022; 13:984929. [PMID: 36313760 PMCID: PMC9606414 DOI: 10.3389/fendo.2022.984929] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Accepted: 09/26/2022] [Indexed: 11/13/2022] Open
Abstract
The choroid plexus (CP) is a cerebral structure located in the ventricles that functions in producing most of the brain's cerebrospinal fluid (CSF) and transporting proteins and immune cells. Alterations in CP structure and function has been implicated in several pathologies including aging, multiple sclerosis, Alzheimer's disease, and stroke. However, identification of changes in the CP remains poorly characterized in obesity, one of the main risk factors of neurodegeneration, including in the absence of frank central nervous system alterations. Our goal here was to characterize the association between obesity, measured by the body mass index (BMI) or waist circumference (WC) metrics, and CP microstructure and volume, assessed using advanced magnetic resonance imaging (MRI) methodology. This cross-sectional study was performed in the clinical unit of the National Institute on Aging and included a participant population of 123 cognitively unimpaired individuals spanning the age range of 22 - 94 years. Automated segmentation methods from FreeSurfer were used to identify the CP structure. Our analysis included volumetric measurements, quantitative relaxometry measures (T 1 and T 2), and the diffusion tensor imaging (DTI) measure of mean diffusivity (MD). Strong positive associations were observed between WC and all MRI metrics, as well as CP volume. When comparing groups based on the established cutoff point by the National Institutes of Health for WC, a modest difference in MD and a significant difference in T 1 values were observed between obese and lean individuals. We also found differences in T1 and MD between obese and overweight individuals as defined using the BMI cutoff. We conjecture that these observations in CP volume and microstructure are due to obesity-induced inflammation, diet, or, very likely, dysregulations in leptin binding and transport. These findings demonstrate that obesity is strongly associated with a decline in CP microstructural integrity. We expect that this work will lay the foundation for further investigations on obesity-induced alterations in CP structure and function.
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Affiliation(s)
- Joseph S R Alisch
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Josephine M Egan
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
| | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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Cai LY, Yang Q, Kanakaraj P, Nath V, Newton AT, Edmonson HA, Luci J, Conrad BN, Price GR, Hansen CB, Kerley CI, Ramadass K, Yeh FC, Kang H, Garyfallidis E, Descoteaux M, Rheault F, Schilling KG, Landman BA. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI. Magn Reson Med 2021; 86:3304-3320. [PMID: 34270123 PMCID: PMC9087815 DOI: 10.1002/mrm.28926] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
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Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Praitayini Kanakaraj
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Allen T. Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jeffrey Luci
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Cailey I. Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
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Ingalhalikar M, Shinde S, Karmarkar A, Rajan A, Rangaprakash D, Deshpande G. Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset. IEEE Trans Biomed Eng 2021; 68:3628-3637. [PMID: 33989150 PMCID: PMC8696194 DOI: 10.1109/tbme.2021.3080259] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized. METHODS We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data. RESULTS Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism. CONCLUSION Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders. SIGNIFICANCE ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
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