Diffusion MRI abnormalities detection with orientation distribution functions: A multiple sclerosis longitudinal study

Abstract : We propose a new algorithm for the voxelwise analysis of orientation distribution functions between one image and a group of reference images. It relies on a generic framework for the comparison of diffusion probabilities on the sphere, sampled from the underlying models. We demonstrate that this method, combined to dimensionality reduction through a principal component analysis, allows for more robust detection of lesions on simulated data when compared to classical tensor-based analysis. We then demonstrate the efficiency of this pipeline on the longitudinal comparison of multiple sclerosis patients at an early stage of the disease: right after their first clinically isolated syndrome (CIS) and three months later. We demonstrate the pre-dictive value of ODF-based scores for the early detection of lesions that will appear or heal.
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Medical Image Analysis, Elsevier, 2015, 22 (1), pp.114-123. 〈10.1016/j.media.2015.02.005〉
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Olivier Commowick, Adil Maarouf, Jean-Christophe Ferré, Jean-Philippe Ranjeva, Gilles Edan, et al.. Diffusion MRI abnormalities detection with orientation distribution functions: A multiple sclerosis longitudinal study. Medical Image Analysis, Elsevier, 2015, 22 (1), pp.114-123. 〈10.1016/j.media.2015.02.005〉. 〈inserm-01134107〉

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