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Proceedings Paper

Robust detection of multiple sclerosis lesions from intensity-normalized multi-channel MRI
Author(s): Yogesh Karpate; Olivier Commowick; Christian Barillot
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Paper Abstract

Multiple sclerosis (MS) is a disease with heterogeneous evolution among the patients. Quantitative analysis of longitudinal Magnetic Resonance Images (MRI) provides a spatial analysis of the brain tissues which may lead to the discovery of biomarkers of disease evolution. Better understanding of the disease will lead to a better discovery of pathogenic mechanisms, allowing for patient-adapted therapeutic strategies. To characterize MS lesions, we propose a novel paradigm to detect white matter lesions based on a statistical framework. It aims at studying the benefits of using multi-channel MRI to detect statistically significant differences between each individual MS patient and a database of control subjects. This framework consists in two components. First, intensity standardization is conducted to minimize the inter-subject intensity difference arising from variability of the acquisition process and different scanners. The intensity normalization maps parameters obtained using a robust Gaussian Mixture Model (GMM) estimation not affected by the presence of MS lesions. The second part studies the comparison of multi-channel MRI of MS patients with respect to an atlas built from the control subjects, thereby allowing us to look for differences in normal appearing white matter, in and around the lesions of each patient. Experimental results demonstrate that our technique accurately detects significant differences in lesions consequently improving the results of MS lesion detection.

Paper Details

Date Published: 20 March 2015
PDF: 7 pages
Proc. SPIE 9413, Medical Imaging 2015: Image Processing, 941314 (20 March 2015); doi: 10.1117/12.2082032
Show Author Affiliations
Yogesh Karpate, VISAGES: INSERM, CNRS, INRIA, Univ. of Rennes 1 (France)
Olivier Commowick, VISAGES: INSERM, CNRS, INRIA, Univ. of Rennes 1 (France)
Christian Barillot, VISAGES: INSERM, CNRS, INRIA, Univ. of Rennes 1 (France)


Published in SPIE Proceedings Vol. 9413:
Medical Imaging 2015: Image Processing
Sébastien Ourselin; Martin A. Styner, Editor(s)

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