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Diffusion MRI processing for multi-compartment characterization of brain pathology

Diffusion weighted imaging (DWI) is a specific type of MRI acquisition based on the direction of diffusion of the brain water molecules. It allows, through several acquisitions, to model the brain microstructure, as white matter, which is significantly smaller than the voxel-resolution.

To acquire a large number of images in a clinical setting, very-fast acquisition techniques are required as single-shot imaging. However these acquisitions suffer locally large distortions. We propose a block-matching registration method based on the acquisition of images with opposite phase-encoding directions (PED).
This technique specially designed for Echo-Planar Images (EPI) robustly correct images and provides a deformation field. This field is applicable to an entire DWI series from only one reversed EPI allowing distortion correction with a minimal acquisition time cost. This registration algorithm has been validated both on phantom and on in-vivo data and is available in our source medical image processing toolbox Anima.

From these diffusion images, we are able to construct multi-compartments models (MCM) which can represent complex brain microstructure. Doing registration, averaging and atlas creation on these MCM images is required to perform studies and statistic analyses. We propose a general method to interpolate MCM as a simplification problem based on spectral clustering. This technique, which is adaptable for any MCM, has been validated on both synthetic and real data. Then, from a registered dataset, we performed a patient to population analysis at a voxel-level computing statistics on MCM parameters. Specifically designed tractography can also be used to make analysis, following tracks, based on individual anisotropic compartments. All these tools are designed and used on real data and contribute to the search of biomakers for brain diseases such as multiple sclerosis.

Speaker: 
Renaud Hedouin
Date: 
Monday, 12. June 2017 - 13:30
Place: 
Salles des thèses, université Rennes 1 batiment 2 au dessus de l'accueil.
Defense Type: 
Composition of jury: 
Simon Warfield du Children's Hospital : simon.warfield@chlidrens.harvard.edu
Isabelle Berry du CHU de Toulouse : berry.i@chu-toulouse.fr
Jean-Philippe Thiran de l'Ecole Polytechnique Fédérale de Lausanne: jean-philippe.thiran@epfl.ch
Rachid Deriche de l'INRIA Sophia-Antipolis  : rachid.deriche@inria.fr
Patrick Perez de Technicolor Rennes : patrick.perez@technicolor.com