Multivariate Statistics for Detection of MS Activity in Serial Multimodal MR Images
Abstract
We present multivariate statistics to detect intensity changes in longitudinal, multimodal, three-dimensional MRI data from patients with multiple sclerosis (MS). Working on a voxel-by-voxel basis, and considering that there is at most one such change-point in the time series of MR images, two complementary statistics are given, which aim at detecting disease activity. We show how to derive these statistics in a Neyman-Pearson framework, by computing ratios of data likelihood under null and alternative hypotheses. Preliminary results show that it is possible to detect both lesion activity and brain atrophy in this framework.