PhD defence L. (Laura) Núñez González

Dissertation in short:

The aim of this thesis was to gain insight into fast multi-parametric quantitative MR techniques and bring them closer to the clinical routine. As an initial step, we evaluated the performance of QRAPMASTER and MRF-vFA. These techniques showed good repeatability in phantom and in-vivo experiments, but QRAPMASTER was more accurate than MRF-vFA. 

The next step was to evaluate these techniques on patients with glioma, acquiring images using a customized version of QRAPMASTER (MAGiC) during the clinical protocol before injecting any contrast-agent. We showed that it is possible to differentiate the pre-contrast T1 and T2 values between tumoral tissue without T1-enhancement, tumoral tissue with T1-enhancement, and normal white matter.

Furthermore, we showed two technical innovations. First, we showed the feasibility and repeatability of a multi-component analysis in highly-undersampled MRF in-vivo data. We obtained white matter, gray matter, cerebrospinal-fluid, and myelin water fraction maps. The results of the Sparsity Promoting Iterative Joint Non-negative least squares algorithm applied to MRF data were promising regarding parameter estimation and tissue-fraction maps segmentation.

The second innovation was a completely new approach and sequence for fast multi-parametric quantitative MR. This sequence is called  "Multiphase balance non-Steady State Free Precession" (MP-b-nSSFP) and it is sensitive to all the intrinsic (PD, T1, T2) and extrinsic (B0, B1+) parameters allowing the simultaneous estimation of all of them.