INTRODUCTION: As neurodegeneration is recognized as a major contributor to disability in multiple sclerosis (MS), brain atrophy quantification could have a high added value in clinical practice to assess treatment efficacy and disease progression, provided that it has a sufficiently low measurement error to draw meaningful conclusions for an individual patient. METHOD: In this paper, we present an automated longitudinal method based on Jacobian integration for measuring whole-brain and gray matter atrophy based on anatomical magnetic resonance images (MRI), named MSmetrix. MSmetrix is specifically designed to measure atrophy in patients with MS, by including iterative lesion segmentation and lesion filling based on FLAIR and T1-weighted MRI scans. RESULTS: MS metrix is compared with SIENA with respect to test-retest error and consistency, resulting in an average test-retest error on an MS data set of 0.13% (MS metrix) and 0.17% (SIENA) and a consistency error of 0.07% (MS metrix) and 0.05% (SIENA). On a healthy subject data set including physiological variability the test-retest is 0.19% (MS metrix) and 0.31% (SIENA). CONCLUSION: Therefore, we can conclude that MSmetrix could be of added value in clinical practice for the follow-up of treatment and disease progression in MS patients.

Department of Neurology and Center of Clinical Neuroscience Charles University Prague 1st Faculty of Medicine and General University Hospital Prague Czech Republic

Department of Neurology Center for Neurosciences Universitair Ziekenhuis Brussel Vrije Universiteit Brussel Brussel Belgium

Department of Radiology 1st Faculty of Medicine and General University Hospital Charles University Prague Czech Republic

National Multiple Sclerosis Centrum Melsbroek Belgium

R and D icometrix Leuven Belgium

R and Dicometrix Leuven Belgium; BioImaging Lab Universiteit Antwerpen Antwerp Belgium

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Relationship between brain atrophy and disability in a multi-site multiple sclerosis registry

RECLAIM-A retrospective, multicenter observational study aimed at enabling the development of artificial intelligence-driven prognostic models for disease progression in multiple sclerosis