UBC Theses and Dissertations

UBC Theses Logo

UBC Theses and Dissertations

A study of spatial encoding and decoding in MRI Depew, Thomas Andrew

Abstract

Spatial encoding is a key feature (perhaps the key feature) of Magnetic Resonance Imaging (MRI). Since the discovery that magnetic field gradients could be used to localize signal, researchers have sought methods to acquire higher quality images, with greater efficiency. This dissertation investigates three approaches for encoding and decoding the information of MRI to provide useful images for clinical diagnosis. Partial Parallel Imaging (PPI) introduced a framework that enabled faster data acquisition, allowing reduction in scan time or improved resolution. We present a regionally optimized implementation of the popular PPI technique GeneRalised Auto-calibrating Partial Parallel Acquisition (GRAPPA). The regional implementation provides images with lower data recovery error and reduced residual aliasing artifact. We assess a number of image quality metrics that would allow automated selection of the optimal reconstructed image. A number of physical quantities can be mapped via comparison of two images with different sensitivities. When the contrast between these images is smoothly varying, the information may be captured using only a fraction of the k-space data required for full image reconstruction. We present a technique to provide robust recovery of relative information maps between images from minimal k-space data. The effectiveness of the technique is demonstrated through application to phase contrast MRI data. Many MRI applications are limited by acquisition in 2D multislice mode. In this regime, the slice direction typically suffers lower resolution than the in-plane directions. We present three strategies to improve through-plane resolution. The relative merits of each technique are investigated, and the performance is quantified with standard measures. The implications of the potential artifacts resulting from each technique are discussed.

Item Citations and Data

License

Attribution-NonCommercial-NoDerivs 2.5 Canada

Usage Statistics