A Log-Euclidean Statistical Analysis of DTI Brain Deformations

Andrew Sweet 1 Xavier Pennec 1
1 ASCLEPIOS - Analysis and Simulation of Biomedical Images
CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : Diffusion tensor images (DTIs) provide information about deep white matter anatomy that structural magnetic resonance images typically fail to resolve. Non-linear registration of DTIs provides a way to capture the deformations of these structures that would otherwise go unobserved. Here we use an existing method that fully incorporates a useful vector space parameterization of diffeomorphisms, thereby allowing simple and well defined calculation of deformation statistics. An initial analysis of the statistics produced by registration of a group of 37 HIV/AIDS patients illustrates principal modes of deformation that are anatomically meaningful and that corroborate with previous findings. The registration method is developed by incorporating these modes into a statistical regularization criterion. Even though initial results suggest this new criterion over-constrains the registration method, we discuss plausible ways to address this.
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Communication dans un congrès
MICCAI 2010 Workshop on Computational Diffusion MRI, Sep 2010, Beijing, China. 〈http://cmic.cs.ucl.ac.uk/cdmri10/〉
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Andrew Sweet, Xavier Pennec. A Log-Euclidean Statistical Analysis of DTI Brain Deformations. MICCAI 2010 Workshop on Computational Diffusion MRI, Sep 2010, Beijing, China. 〈http://cmic.cs.ucl.ac.uk/cdmri10/〉. 〈hal-01525407〉

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