Compressive sensing in diffusion MRI
type: Dataset
description: This thesis is dedicated to the development of new acquisition and processing methods in diffusion MRI (dMRI) to characterize the diffusion of water molecules in white matter fiber bundles at the scale of a voxel. In particular, we focus our attention on the accurate recovery of the Ensemble Average Propagator (EAP), which represents the full 3D displacement of water molecule diffusion. Diffusion models such that the Diffusion Tensor or the Orientation Distribution Function (ODF) are largely used in the dMRI community in order to quantify water molecule diffusion. These models are partial EAP representations and have been developed due to the small number of measurement required for their estimations. It is thus of utmost importance to be able to accurately compute the EAP and order to acquire a better understanding of the brain mechanisms and to improve the diagnosis of neurological disorders. Estimating the full 3D EAP requires the acquisition of many diffusion images sensitized todifferent orientations in the q-space, which render the estimation of the EAP impossible in most of the clinical dMRI scanner. A surge of interest has been seen in order to decrease this time for acquisition. Some works focus on the development of new and efficient acquisition sequences. In this thesis, we use sparse coding techniques, and in particular Compressive Sensing (CS) to accelerate the computation of the EAP. Multiple aspects of the CS theory and its application to dMRI are presented in this thesis.
identifier: NNT: 2013NICE4061
issued: 2026-05-07T21:09:56.193352
language: en
modified: 2026-05-07T21:09:56.193356
contact point: Organization
  fn: CCSD
keyword: acquisition-compressee
keyword: apprentissage-de-dictionnaire
keyword: compressive-sensing
keyword: dictionary-learning
keyword: diffusion-mri
keyword: diffusion-spectrum-imaging
keyword: ensemble-average-propagator
keyword: infoeu-reposemanticsdoctoralthesis
keyword: irm-de-diffusion
keyword: propagateur-de-diffusion
keyword: q-ball-imaging
keyword: q-space-sampling
keyword: reconstruction-parcimonieuse
keyword: sparse-coding
keyword: spiotherengineering-sciences-physicsother
keyword: theses
landing page: https://theses.hal.science/tel-00908369

Compressive sensing in diffusion MRI
type: Distribution
format: HTML
issued: 2026-05-07T21:09:56.195473
modified: 2026-05-07T21:09:56.167981
access URL: https://theses.hal.science/tel-00908369

test_moissonnage_selune
type: Agent