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Multivariate Statistics for Detection of MS Activity in Serial Multimodal MR Images

Sylvain Prima 1, 2 Douglas L. Arnold 2 Louis D. Collins 2 
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
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.
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Submitted on : Wednesday, October 19, 2011 - 2:28:50 PM
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Sylvain Prima, Douglas L. Arnold, Louis D. Collins. Multivariate Statistics for Detection of MS Activity in Serial Multimodal MR Images. Medical Image Computing and Computer-Assisted Intervention (MICCAI'03), 2003, Tokyo, Japan. pp.663-670, ⟨10.1007/978-3-540-39899-8_81⟩. ⟨inserm-00633804⟩



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