Bayesian Classification of Multiple Sclerosis Lesions in Longitudinal MRI Using Subtraction Images
(0) Accurate and precise identification of multiple sclerosis (MS) lesions in longitudinal MRI is important for monitoring disease progression and for assessing treatment effects. We present a probabilistic framework to automatically detect new, enlarging and resolving lesions in longitudinal scans of MS patients based on multimodal subtraction magnetic resonance (MR) images. Our Bayesian framework overcomes registration artifact by explicitly modeling the variability in the difference images, the tissue transitions, and the neighbourhood classes in the form of likelihoods, and by embedding a classification of a reference scan as a prior. Our method was evaluated on (a) a scan-rescan data set consisting of 3 MS patients and (b) a multicenter clinical data set consisting of 212 scans from 89 RRMS (relapsing-remitting MS) patients. The proposed method is shown to identify MS lesions in longitudinal MRI with a high degree of precision while remaining sensitive to lesion activity.
Colm Elliott Simon J.Francis Douglas L.Arnold D.Louis Collins Tal Arbel
Centre for Intelligent Machines, McGill University, Canada Montreal Neurological Institute, McGill University, Canada NeuroRx Research, Montreal, Canada
国际会议
北京
英文
290-297
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）