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Conference Papers Year : 2010

A Log-Euclidean Statistical Analysis of DTI Brain Deformations

Andrew Sweet
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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.

Domains

Medical Imaging
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Dates and versions

hal-01525407 , version 1 (20-05-2017)

Identifiers

  • HAL Id : hal-01525407 , version 1

Cite

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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