Longitudinal Analysis of White Matter Lesions in Multiple Sclerosis using Mixture Modeling at 7 Tesla

Motivation: Analyzing multiple sclerosis (MS) lesions longitudinally is challenging, and requires consistent and robust imaging, processing, and statistical methods. Conventional binary lesion segmentations might overlook significant pathology changes as degeneration is continuous. Goal(s): To track subtle changes in MS lesions using statistically derived metrics. Approach: We introduced a two-component Mixture Model (MM) to track longitudinal changes qT1 with regions of interest (ROIs) and compared it with conventional image metrics. Results: In our longitudinal analysis, the MM approach uncovered changes missed by traditional methods suggesting MM may help capture and understand longitudinal evolution of MS lesions. Impact: Our novel mixture modeling analysis approach may untangle unique aspects of biological processes in MS lesion evolution, offering a valuable alternative to conventional image analysis methods.