Abstract
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 intrasubject 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.
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Keywords
- Multiple Sclerosis
- Multiple Sclerosis Patient
- Expand Disability Status Scale
- Brain Atrophy
- Least Trim Square
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Prima, S., Ayache, N., Janke, A., Francis, S.J., Arnold, D.L., Collins, D.L. (2002). Statistical Analysis of Longitudinal MRI Data: Applications for Detection of Disease Activity in MS. In: Dohi, T., Kikinis, R. (eds) Medical Image Computing and Computer-Assisted Intervention — MICCAI 2002. MICCAI 2002. Lecture Notes in Computer Science, vol 2488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45786-0_45
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