Optimization of acquisition in diffusion MRI towards quantitative connectivity

Submitted by Emmanuel CARUYER on Thu 28/04/2022 - 14:00
Date of the beginning of the PhD (if already known)
IRISA - UMR 6074
Description of the subject

Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique sensible to the self-displacement of water molecules. In brain imaging, it provdes invaluable information on properties of the brain tissue organization at a micrometer scale, several orders of magnitude below the typical resolution of conventional MRI. In white matter, due to the anisotropic nature of diffusion within axonal fiber bundles, it is possible to trace the path of major tracts using tractography, therefore providing an in vivo image of the global brain structural connectivity. The reconstruction of such a high level representation of the brain as a graph paved the way to an unprecedented description of the normal, developing and aging brain and the detection of subtle changes in the global organisation of the brain associated with pathology. However, current tractography algorithms still present inherent challenges, such as a large number of false positives and a relatively poor quantitative estimation of connection strength.

Over the past 5 years, there has been a growing interest of the community in developing advanced acquisition schemes aiming at a more accurate reconstruction of tissue properties locally, thus providing a better sensitivity and specificity to focal changes related to pathology and paving the way for better biomarkers. In particular, the use of generalized diffusion-encoding gradient waveforms were shown to increase the sensitivity to specific microstructure parameters. Intuitively, a better characterization of microstructure locally shall help in the reconstruction of the connectome; yet to date very few studies have focused on the impact of the acquisition scheme on the reconstruction of the connectome. The main objective of this project will be to bridge the gap between acquisition design in the one hand and quantitative connectivity mapping in the other hand.

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Lastname, Firstname
Caruyer, Emmanuel
Type of supervision

Lastname, Firstname
Corouge, Isabelle
Type of supervision
Supervisor (optional)
UMR 6074
Caruyer, Emmanuel
image processing, signal processing, compressed sensing, neuroimaging, diffusion MRI