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Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
Deep Learning For Task-Based Image Quality Assessment In Medical Imaging, Weimin Zhou
McKelvey School of Engineering Theses & Dissertations
It has been advocated to use objective measures of image quality (IQ) for assessing and optimizing medical imaging systems. Objective measures of IQ quantify the performance of an observer at a specific diagnostic task. Binary signal detection tasks and joint signal detection and localization (detection-localization) tasks are commonly considered in medical imaging. When optimizing imaging systems for binary signal detection tasks, the performance of the Bayesian Ideal Observer (IO) has been advocated for use as a figure-of-merit (FOM). The IO maximizes the observer performance that is summarized by the receiver operating characteristic (ROC) curve. When signal detection-localization tasks are considered, …
Multi-Gpu Acceleration Of Iterative X-Ray Ct Image Reconstruction, Ayan Mitra
Multi-Gpu Acceleration Of Iterative X-Ray Ct Image Reconstruction, Ayan Mitra
McKelvey School of Engineering Theses & Dissertations
X-ray computed tomography is a widely used medical imaging modality for screening and diagnosing diseases and for image-guided radiation therapy treatment planning. Statistical iterative reconstruction (SIR) algorithms have the potential to significantly reduce image artifacts by minimizing a cost function that models the physics and statistics of the data acquisition process in X-ray CT. SIR algorithms have superior performance compared to traditional analytical reconstructions for a wide range of applications including nonstandard geometries arising from irregular sampling, limited angular range, missing data, and low-dose CT. The main hurdle for the widespread adoption of SIR algorithms in multislice X-ray CT reconstruction …