Pooled analysis of multiple sclerosis findings on multisite 7 Tesla MRI: Protocol and initial observations

• Published in: Human brain mapping 2024-08, Vol.45 (12), p.e26816-n/a
• Main Authors: Harrison, Daniel M., Choi, Seongjin, Bakshi, Rohit, Beck, Erin S., Callen, Alexis M., Chu, Renxin, Silva, Jonadab Dos Santos, Fetco, Dumitru, Greenwald, Matthew, Kolind, Shannon, Narayanan, Sridar, Okar, Serhat V., Quattrucci, Molly K., Reich, Daniel S., Rudko, David, Russell‐Schulz, Bretta, Schindler, Matthew K., Tauhid, Shahamat, Traboulsee, Anthony, Vavasour, Zachary, Zurawski, Jonathan D.
• Format: Article
• Language: English
• Subjects: 7 T; Adult; Annotations; Brain - diagnostic imaging; Brain - pathology; Collaboration; Datasets; Female; Humans; Image acquisition; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image quality; Image segmentation; Inflammation; Lesions; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Magnetic Resonance Imaging - standards; Male; Medical imaging; Middle Aged; multicenter; Multiple sclerosis; Multiple Sclerosis - diagnostic imaging; Multiple Sclerosis - pathology; Neuroimaging; Pathology; Retrospective Studies; Similarity coefficient method; Substantia grisea; Working groups
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.26816

Although 7 T MRI research has contributed much to our understanding of multiple sclerosis (MS) pathology, most prior data has come from small, single‐center studies with varying methods. In order to truly know if such findings have widespread applicability, multicenter methods and studies are needed. To address this, members of the North American Imaging in MS (NAIMS) Cooperative worked together to create a multicenter collaborative study of 7 T MRI in MS. In this manuscript, we describe the methods we have developed for the purpose of pooling together a large, retrospective dataset of 7 T MRIs acquired in multiple MS studies at five institutions. To date, this group has contributed five‐hundred and twenty‐eight 7 T MRI scans from 350 individuals with MS to a common data repository, with plans to continue to increase this sample size in the coming years. We have developed unified methods for image processing for data harmonization and lesion identification/segmentation. We report here our initial observations on intersite differences in acquisition, which includes site/device differences in brain coverage and image quality. We also report on the development of our methods and training of image evaluators, which resulted in median Dice Similarity Coefficients for trained raters' annotation of cortical and deep gray matter lesions, paramagnetic rim lesions, and meningeal enhancement between 0.73 and 0.82 compared to final consensus masks. We expect this publication to act as a resource for other investigators aiming to combine multicenter 7 T MRI datasets for the study of MS, in addition to providing a methodological reference for all future analysis projects to stem from the development of this dataset. Harrison et al. describe methods for data pooling and analysis developed by team members from five institutions performing 7 T MRI research in multiple sclerosis. This manuscript will act as a reference for future analyses performed on a large 7‐T MRI dataset, which will eventually contain more than 1000 scans.