Qing-San Xiang

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 robustrecovery 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.

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Clinical procedures such as image-guided surgery and assessment of tissue regions near implants can greatly benefit from magnetic resonance imaging (MRI) near metals. Unfortunately, metals perturb the MRI magnetic field, causing deleterious image distortion, signal loss, and signal overlap artifacts. Several techniques have been developed to correct these artifacts, but those which provide comprehensive solutions require scan times which are too lengthy for time-constrained imaging applications. This study outlines an approach for reconstructing metal artifact corrected images with unique contrast in minimal scan time. First, a technique was developed to completely eliminate distortion in spin-echo images of metals using added phase gradients along the distorted dimension. Attempts to generalize this technique for the correction of signal loss and overlap artifacts faced difficulties. However, these investigations provoked the discovery of a new framework for imaging near metals.This framework is based on the balanced steady state free precession (bSSFP) sequence, which generates images near metals with little to no signal overlap, signal loss, and distortion. Two methods were developed to completely remove problematic signal modulation and banding artifacts using four images with variable radiofrequency phase cycling. One technique employs geometric intuition, and the other an algebraic derivation to calculate unique expressions for the same base demodulated signal. The variable performance of the two techniques on noisy data inspires their variance-weighted summation for robust and high performance image reconstruction.Complementary techniques for reduction of bSSFP signal loss, distortion, and scan time were also devised. Shimming with imaging gradients is shown to recover biphasic signal loss at the cost of extra scan time. Residual distortion is corrected using the phase of the geometric demodulation solution. Two techniques reduce the amount of image data required for signal demodulation. When all developments are considered, a customized balance of image fidelity and scan time may be achieved. Images without bSSFP banding or distortion may be formed of regions close to metals, and residual signal loss may be recovered at the expense of longer scan time. Additional data reduction measures complete the described framework’s capacity for flexible and efficient imaging near metals.

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