Biomedical Engineering and Bioengineering Commons^™

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-Dimensional Extension Of The Alternating Minimization Algorithm In X-Ray Computed Tomography, Jingwei Lu

McKelvey School of Engineering Theses & Dissertations

X-ray computed tomography (CT) is an important and effective tool in medical and industrial

imaging applications. The state-of-the-art methods to reconstruct CT images have had

great development but also face challenges. This dissertation derives novel algorithms to

reduce bias and metal artifacts in a wide variety of imaging modalities and increase performance

in low-dose scenarios.

The most widely available CT systems still use the single-energy CT (SECT), which is

good at showing the anatomic structure of the patient body. However, in SECT image

reconstruction, energy-related information is lost. In applications like radiation treatment

planning and dose prediction, accurate energy-related information …

Multi-Dimensional Extension Of The Alternating Minimization Algorithm In X-Ray Computed Tomography, Jingwei Lu

McKelvey School of Engineering Theses & Dissertations

X-ray computed tomography (CT) is an important and effective tool in medical and industrial imaging applications. The state-of-the-art methods to reconstruct CT images have had great development but also face challenges. This dissertation derives novel algorithms to reduce bias and metal artifacts in a wide variety of imaging modalities and increase performance in low-dose scenarios. The most widely available CT systems still use the single-energy CT (SECT), which is good at showing the anatomic structure of the patient body. However, in SECT image reconstruction, energy-related information is lost. In applications like radiation treatment planning and dose prediction, accurate energy-related information …