Advancing diffusion MRI: improving image quality and getting rid of the fat

Abstract

This thesis advances the scope of DWI in MRI, with a particular focus on water/fat separated msh-EPI based on chemical shift encoding. It outlines ways to simplify the data acquisition, boiling it down to the essential, producing diffusion weighted images with chemical shift encoding on top in a very efficient manner. It presents different algorithms for effective water/fat separation and improved image reconstruction. These methods address challenges such as spatial shifts of fat signals and motion-induced shot-to-shot phase variations to improve the image quality and accuracy of DWI, and shows the application in different anatomical regions, such as leg, brain, head-neck and prostate. The research combines advanced modeling techniques, such as SENSE-based approaches and structured low-rank concepts, to achieve superior image quality and make significant contributions to the application of DWI in potential clinical diagnostics.