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Conference papers

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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Submitted on : Saturday, May 20, 2017 - 12:37:45 PM
Last modification on : Saturday, June 25, 2022 - 11:26:40 PM
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  • HAL Id : hal-01525407, version 1



Andrew Sweet, Xavier Pennec. A Log-Euclidean Statistical Analysis of DTI Brain Deformations. MICCAI 2010 Workshop on Computational Diffusion MRI, Sep 2010, Beijing, China. ⟨hal-01525407⟩



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