Ultra-low-field MRI for bedside imaging of severe multiple sclerosis

Background: Severe multiple sclerosis (MS) presents challenges for clinical research due to patient mobility constraints and specialized care needs. Traditional MRI studies often exclude this population but portable, ultra-low-field (ULF) MRI at 0.064 T enables bedside imaging. Objectives: To (i) compare ULF MRI volumetry with standard 3 T, (ii) assess the capability of portable ULF MRI in characterizing severe MS. Methods: This prospective study enrolled two cohorts. Cohort 1 included healthy controls (HC) and people with MS (pwMS). Cohort 2 enrolled individuals with progressive MS, with either severe or less severe MS. Both cohorts underwent ULF MRI while those in Cohort 1 were also scanned on 3 T MRI. Clinical assessments included disability and cognition. MRI data was processed using both conventional (e.g., SIENAX) and AI-driven (e.g., WMH-SynthSeg) techniques. Group comparisons and MRI-clinical associations were assessed. Results: Cohort 1 included 14 HCs (39 ± 13 years, 11 females) and 33 pwMS (32 ± 10 years, 24 females). Cohort 2 included 24 severe MS individuals (62 ± 10, 15 females) and 16 less severe MS individuals (61 years ± 11, 8 females). MRI passed quality control except for scans from 2 individuals in Cohort 2. In Cohort 1, 3 T and ULF MRI showed robust disease differences, with strongest effect sizes for ULF MRI-derived whole brain (partial η² = 0.264), cortical gray matter (GM) (partial η² = 0.234), and thalamic volumes (partial η² = 0.266) from WMH-SynthSeg. In terms of severe versus less severe disease in Cohort 2, the largest effect sizes were obtained with SIENAX-derived cortical GM volume (partial η² = 0.349) and whole brain volume (partial η² = 0.290), while WMH-SynthSeg yielded the highest effect size for WM volume (partial η² = 0.209). For clinical outcomes, associations were dependent on processing method, although SIENAX yielded the most consistent correlations in Cohort 2. Conclusion: Portable, ULF MRI is feasible in pwMS and provides insights into late-stage neurodegeneration in severe MS.