Spatial normalization of multiple sclerosis brain MRI data depends on analysis method and software package |
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| Affiliation: | 2. Department of Rehabilitation Medicine, Hyogo College of Medicine, Nishinomiya, Hyogo, Japan;1. Department of Radiology, University of Manitoba, Winnipeg, MB, Canada;2. Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada;3. Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada;4. Department of Physiology and Pathophysiology, University of Manitoba, Winnipeg, MB, Canada;5. Biomedical Engineering Graduate Program, University of Manitoba, Winnipeg, MB, Canada;6. Department of Psychological and Brain Sciences, Johns Hopkins University, Baltimore, MD, USA;1. Department of Radiology and Medical Imaging, University of Virginia, Charlottesville, VA, USA;2. Penn Image Computing and Science Laboratory, University of Pennsylvania, Philadelphia, PA, USA;3. Sage Bionetworks, Seattle, WA, USA;4. Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway |
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| Abstract: | BackgroundSpatially normalizing brain MRI data to a template is commonly performed to facilitate comparisons between individuals or groups. However, the presence of multiple sclerosis (MS) lesions and other MS-related brain pathologies may compromise the performance of automated spatial normalization procedures. We therefore aimed to systematically compare five commonly used spatial normalization methods for brain MRI – including linear (affine), and nonlinear MRIStudio (LDDMM), FSL (FNIRT), ANTs (SyN), and SPM (CAT12) algorithms – to evaluate their performance in the presence of MS-related pathologies.Methods3 Tesla MRI images (T1-weighted and T2-FLAIR) were obtained for 20 participants with MS from an ongoing cohort study (used to assess a real dataset) and 1 healthy control participant (used to create a simulated lesion dataset). Both raw and lesion-filled versions of each participant's T1-weighted brain images were warped to the Montreal Neurological Institute (MNI) template using all five normalization approaches for the real dataset, and the same procedure was then repeated using the simulated lesion dataset (i.e., total of 400 spatial normalizations). As an additional quality-assurance check, the resulting deformations were also applied to the corresponding lesion masks to evaluate how each processing pipeline handled focal white matter lesions. For each normalization approach, inter-subject variability (across normalized T1-weighted images) was quantified using both mutual information (MI) and coefficient of variation (COV), and the corresponding normalized lesion volumes were evaluated using paired-sample t-tests.ResultsAll four nonlinear warping methods outperformed conventional linear normalization, with SPM (CAT12) yielding the highest MI values, lowest COV values, and proportionately-scaled lesion volumes. Although lesion-filling improved spatial normalization accuracy for each of the methods tested, these effects were small compared to differences between normalization algorithms.ConclusionsSPM (CAT12) warping, ideally combined with lesion-filling, is recommended for use in future MS brain imaging studies requiring spatial normalization. |
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