Evaluation of Self-calibrated Cartesian Sense Methods for Parallel MRI.

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ProQuest, 2009 - 243 pages
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One of most commonly used parallel imaging methods is Sensitivity Encoding (SENSE) developed by Preussman, et al., in 1999, based on which a series of algorithms have been developed over the decade. Most of the algorithms focused on the noise issues in reconstructed images, while others also involved the concerns in phase component of the complex MRI image. This Thesis implements five different SENSE algorithms and makes systematic comparisons among them, with evaluations given both visually and quantitatively. This work also addresses the reconstruction speed of each of the algorithms, which is an important feature for clinical MRI. The results of those comparisons then lead to the investigation into noise and aliasing artifacts, which are the two major problems in SENSE reconstruction. Through a series of experiments, this thesis discusses the various sources of noise and artifacts as well as the varying effects of these sources on the resulting images. This work shows that noise and aliasing artifacts are actually two different kinds of perturbations to reconstructed images from very different sources. Finally, one problem remaining in SENSE is that there is no standard method of quantitative assessment of aliasing artifacts in the final images, other than a general SNR value or the g-factor map. However, the SNR values can be heavily biased by the chosen of regions of interest and the g-factor map is a measurement of noise amplification of the noise across the image, both of which are not assessment specifically for aliasing artifacts. To address this issue, this thesis introduces a metric for aliasing artifacts in the reconstructed images for research purposes.
  

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Contents

INTRODUCTION
1
Algorithms for SENSE Improvement
14
SENSITIVITY MAPS
48
TEST OF DIFFERENT ALGORITHMS ON REAL MRI DATA
79
DEEPER INVESTIGATION INTO NOISE ALIASING
131
CONCLUSIONS
190
APPENDIX MATLAB FUNCTIONS
197
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