Ultra-low-field MRI for imaging of severe multiple sclerosis: a case-controlled study
Authors
Affiliations (1)
Affiliations (1)
- Buffalo Neuroimaging Analysis Center, Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, State University of New
Abstract
BackgroundSevere multiple sclerosis (MS) presents challenges for clinical research due to mobility constraints and specialized care needs. Traditional MRI studies often exclude this population, limiting understanding of severe MS progression. Portable, ultra-low-field MRI enables bedside imaging. ObjectivesTo (i) assess the feasibility of portable MRI in severe MS, (ii) compare measurement approaches for automated tissue volumetry from ultra-low-field MRI. MethodsThis prospective study enrolled 40 progressive MS patients (24 severely disabled, 16 less severe) from academic and skilled nursing settings. Participants underwent 0.064T MRI for tissue volumetry using conventional and artificial intelligence (AI)-driven segmentation. Clinical assessments included physical disability and cognition. Group comparisons and MRI-clinical associations were assessed. ResultsMRI passed rigorous quality control, reflecting complete brain coverage and lack of motion artifact, in 38/40 participants. In terms of severe versus less severe disease, the largest effect sizes were obtained with conventionally-calculated gray matter (GM) volume (partial 2=0.360), cortical GM volume (partial 2=0.349), and whole brain volume (partial 2=0.290) while an AI-based approach yielded the highest effect size for white matter volume (partial 2=0.209). For clinical outcomes, the most consistent associations were found using conventional processing while AI-based methods were dependent on algorithm and input image, especially for cortical GM volume. ConclusionPortable, ultralow-field MRI is a feasible bedside tool that can provide insights into late-stage neurodegeneration in individuals living with severe MS. However, careful consideration is required in implementing tissue volumetry pipelines as findings are heavily dependent on the choice of algorithm and input.