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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https://www.hal.inserm.fr/inserm-01134107
Contributor : Olivier Commowick <>
Submitted on : Sunday, March 22, 2015 - 5:04:29 PM
Last modification on : Monday, March 4, 2019 - 2:08:07 PM
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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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