Disentangling Tissue Microstructure with Magnetic Resonance Imaging
M. ZARAPICO ROMERO, M.D. Miguel · 2018
This thesis takes MRI a step further to study brain tissue microstructure. The dMRI signal is reformulated in a Blind Source Separation framework, enabling the disentanglement of sub-voxel tissue signal components, and the estimation of multiple tissue parameters. Furthermore, a deep learning model is introduced, tackling the partial volume contamination caused by Cerebrospinal Fluid in dMRI. Finally, Quantitative Transient-state Imaging, an ultra-fast acquisition and reconstruction scheme for multiparameter mapping, is extended to a tissue multicompartment model.