Accuracy of Arterial Spin Labeling in Magnetic Resonance Imaging
Zhao, Li, Biomedical Engineering - School of Engineering and Applied Science, University of Virginia
Meyer, Craig, Department of Biomedical Engineering, University of Virginia
Cerebral perfusion imaging provides information of blood supply and reveals functional changes of tissue before structural changes. It can be used to improve the sensitivity and accuracy of stroke and tumor diagnosis. There has been increasing interest in developing a non-contrast perfusion imaging, because of the risk in patients with acute kidney dysfunction. Magnetic resonance imaging provides a safer option of perfusion imaging without a contrast agent: arterial spin labeling (ASL). However, ASL is inherently has low signal-to-noise ratio, which limits the imaging quality and quantification accuracy.
In this work, we improved the ASL imaging by following techniques: accelerated reference parameter mapping by unscented Kalman filter; robust ASL with SNR efficient 3D readout and constrained reconstruction; accurate dynamic ASL with model-based constraint and optimal dynamic ASL experiment design.
PHD (Doctor of Philosophy)
magnetic resonance imaging, arterial spin labeling, parametric mapping, compressed sensing, model-based sparsity, Kalman filter
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