Diffusionsgewichtete und Diffusionstensor-Bildgebung in der Magnetischen Resonanztomographie - Sequenzentwicklung und -optimierung im Fokus der klinischen Anwendung

The importance of Magnetic Resonance Tomography (MRT) arises from the high frequency of water protons in living tissue, the non-invasiveness of the technique and the straightforward spatial encoding of protons by means of additional locally varying magnetic field gradients. During the last two decades, diffusion-weighted imaging (DWI) has added an essential contribution to clinical MR tomography of structural T1- and T2-weighted imaging and to mapping of physiological processes like perfusion and functional activity. DWI uses the self-diffusion (Brownian motion) of water molecules in an inhomogeneous static magnetic field that can be encoded by locally dependent gradient fields. The key feature and importance of MR-DWI results from the fact that the random translatory motion of molecules scans the microscopic tissue structures far beyond the spatial resolution of MR imaging techniques. Furthermore, diffusion as a physical process is independent of magnetic resonance phenomena, but offers similar advantages like high contrast and spatial resolution. The intent of this dissertation covers projects of sequence development and optimization of clinical DWI applications that illustrate the fast evolving development of DWI in medical MR imaging. One focus is set on the combination of DW echo-planar imaging (EPI) and the fluid-attenuated inversion recovery (FLAIR) prepa¬ration. After technical validation, issues of measurement accuracy and signal-to-noise ratio and their implications on estimated contrast parameters like diffusion anisotropy are discussed. A proposed correction term enables immediate acquisition and comparison of standard DW-EPI and FLAIR-DWI in volunteer and patient studies. A preliminary study on patients with astrocytoma reveals the advantages of FLAIR-prepared DW imaging protocols. The second topic of this dissertation explores the extension of the linear diffusion tensor model to high angular resolution DWI (HARDI). The high complexity of white matter