A Spatio-temporal Model-based Statistical Approach to Detect Evolving Multiple Sclerosis Lesions

The effects of new treatments need to be assessed: in the case of multiple sclerosis it is possible to measure those effects by studying temporal lesions’ evolutions in time series of MRI. But it is a laborious task to manually analyze such sets of images. This article proposes a new method to statistically analyze a series of T2-weighted MRI of a patient with multiple sclerosis lesions taking both temporal and spatial coherence into account. The main idea of the method is to fit a temporal parametric model of intensity evolution on each voxel of the series; these estimations give different parameter values in the case of normal and pathological areas. A statistical inference stage makes it possible to determine significant sets of connected voxels corresponding to pathological evolving areas. The significancy is estimated using permutations. Promising results show the feasibility of our approach. On our data sets the evolving lesions were detected and their temporal behavior could be quantified.