System-wide advances in photon-counting CT : corrections, simulations, and image analysis

Getzin, Matthew
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Wang, Ge, 1957-
Intes, Xavier
Wan, Leo Q.
Lai, Rongjie
Fleysher, Roman
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Biomedical engineering
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Attribution-NonCommercial-NoDerivs 3.0 United States
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
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Imaging plays a major role in biomedicine with X-ray computed tomography (CT) as a primary example. X-ray CT becomes increasingly popular since its invention in the 1970’s, now reaching over 100 million scans yearly worldwide. Advances in CT techniques and algorithms have been recently made, enabled by high-performance computing power and novel system hardware. State-of-the-art research CT prototypes have now begun to incorporate dynamic source filters and photo-counting detectors to measure spectral information for low dose, high resolution, and material decomposition. Less than two years ago, the Biomedical Imaging Center group at RPI obtained one of the best preclinical micro-CT scanners – the MARS micro-CT (MARS Bioimaging Ltd., Christchurch, NZ). Equipped with a Medipix3RX cadmium-zinc-telluride (CZT) photon-counting detector, the scanner is capable of simultaneously recording data in 5 distinct energy bins. This thesis provides solid results and physical insights for improving the current photon-counting imaging performance provided by this MARS system through (1) pre-reconstruction, spatial non-uniformity corrections, (2) hardware mechanisms to increase spatial resolution, and (3) post-reconstruction methods for analyzing image regions which contain varying levels of contrast materials. During this research project, a full-scale system simulator was developed in MATLAB as the “ideal” photon-counting detector/system model, which is a significant portion of the thesis. Our major goal is to provide a base for future development f a seamless imaging pipeline, which is from acquisition and preprocessing to reconstruction and post-processing, analysis, as well as material decomposition in a unified deep learning framework.
December 2019
School of Engineering
Dept. of Biomedical Engineering
Rensselaer Polytechnic Institute, Troy, NY
Rensselaer Theses and Dissertations Online Collection
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