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We present a method to detect intensity changes in longitudinal volumetric MRI data from patients with multiple sclerosis (MS). Preprocessing includes spatial and intensity normalization. The intra-subject intensity normalization is achieved using a polynomial least trimmed squares method to match the histograms of all images in the series. Viewing the detection of disease activity in MRI as a change-point problem, we present two statistical tests and apply them to a patient's series of grey-level images on a voxel-by-voxel basis. Results are compared with manual lesion segmentation for one MS patient scanned approximately every 5 months for 5 years. Results are also shown for 12 MS patients with 30 monthly scans.

Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS