Utilisation of the natural self-diffusion of water to make physiological measurements in human brain

Magnetic resonance imaging enables the parameterisation of various physiological processes by comparison of a baseline set of images with a set of images taken after some perturbation of the nuclear spins. By linking this perturbation to a physiological process, changes in signal intensity become a proxy measure of that physiological process. This thesis is concerned with the accuracy and meaning of the measurements made using the natural self-diffusion of water. The first section deals with diffusion tensor measurements, which are three-dimensional measures of water self-diffusion using linear magnetic field gradients. The thesis looks in turn at the effect of signal noise, and partial volume effects on parameters such as the mean diffusivity, the eigenvalues and the fractional anisotropy of the diffusion tensor. The accuracy of the technique, as indicated by the results of numerical simulations, is compared to those acquired from phantom and brain imaging. These results provide an estimate of the dependence of diffusion measurements on technique. The coefficient of variation of diffusion measurements is shown to depend on the metric being used. The voxel volume is shown to have a large effect on the measured anisotropy but not the mean diffusivity. The elevation in diffusion anisotropy observed in acute stroke was then investigated using the eigenvalues of the diffusion tensor, and isotropic and anisotropic tensors. The second section addresses the relationship between gradient-echo and spin-echo dynamic-susceptibility-contrast measurements of cerebral perfusion. The basic contrast measurement is the diffusion of water through pseudo-random magnetic fields created by the injection of a contrast agent. Numerical simulations were used to elucidate the relationship between signal loss and biophysical variables, such as the vessel geometry, the blood volume, and the rate of water self-diffusion.EThOS - Electronic Theses Online ServiceGBUnited Kingdo