Diffusion MRI processing for multi-comportment characterization of brain pathology

Renaud Hédouin 1
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U1228, Inria Rennes – Bretagne Atlantique , IRISA_D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Diffusion weighted imaging (DWI) is a specific type of MRI acquisition based on the direction of diffusion of the brain water molecule. Its allow, through several acquisitions, to model brain microstructure, as white matter, which are significantly smaller than the voxel-resolution. To acquire a large number of images in a clinical use, very-fast acquisition technique are required as single-shot imaging, however these acquisitions suffer local large distortions. We propose a Block-Matching registration method based on a the acquisition of images with opposite phase-encoding directions (PED). This technique specially designs for Echo-Planar Images (EPI), but which could be generic, robustly correct images and provide a deformation field. This field is applicable to an entire DWI series from only one reversed b 0 allowing distortion correction with a minimal time acquisition cost. This registration algorithm has been validated both on a phantom data set 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 could represented complex brain microstructure. We need to do registration, average, create atlas on these MCM to be able to make studies and produce statistic analysis. 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 for both synthetic and real data. Then, from a registered dataset, we made analysis at a voxel-level doing statistic on MCM parameters. Specifically design tractography can also be perform to make analysis, following tracks, based on individual compartment. All these tools are designed and used on real data and contribute to the search of biomakers for brain diseases.
Complete list of metadatas

Cited literature [225 references]  Display  Hide  Download

Contributor : Abes Star <>
Submitted on : Friday, December 1, 2017 - 9:58:10 AM
Last modification on : Monday, March 4, 2019 - 2:07:43 PM


Version validated by the jury (STAR)


  • HAL Id : tel-01548337, version 2


Renaud Hédouin. Diffusion MRI processing for multi-comportment characterization of brain pathology. Signal and Image processing. Université Rennes 1, 2017. English. ⟨NNT : 2017REN1S035⟩. ⟨tel-01548337v2⟩



Record views


Files downloads