A Log-Euclidean Statistical Analysis of DTI Brain Deformations - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year :

A Log-Euclidean Statistical Analysis of DTI Brain Deformations

Andrew Sweet
  • Function : Author
  • PersonId : 1008592

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
Fichier principal
Vignette du fichier
cdmri2010.pdf (4.58 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

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⟩

Collections

INRIA INRIA2
97 View
18 Download

Share

Gmail Facebook Twitter LinkedIn More