Browsing UA Graduate and Undergraduate Research by Subjects
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Lens-coupled X-Ray Imaging SystemsDigital radiography systems are important diagnostic tools for modern medicine. The images are produced when x-ray sensitive materials are coupled directly onto the sensing element of the detector panels. As a result, the size of the detector panels is the same size as the x-ray image. An alternative to the modern DR system is to image the x-ray phosphor screen with a lens onto a digital camera. Potential advantages of this approach include rapid readout, flexible magnification and field of view depending on applications. We have evaluated lens-coupled DR systems for the task of signal detection by analyzing the covariance matrix of the images for three cases, using a perfect detector and lens, when images are affected by blurring due to the lens and screen, and for a signal embedded in a complex random background. We compared the performance of lens-coupled DR systems using three types of digital cameras. These include a scientific CCD, a scientific CMOS, and a prosumer DSLR camera. We found that both the prosumer DSLR and the scientific CMOS have lower noise than the scientific CCD camera by looking at their noise power spectrum. We have built two portable low-cost DR systems, which were used in the field in Nepal and Utah. We have also constructed a lens-coupled CT system, which included a calibration routine and an iterative reconstruction algorithm written in CUDA.
Task-Based Assessment and Optimization of Digital Breast TomosynthesisDigital breast tomosynthesis (DBT) is a new technology for breast cancer screening that promises to complement mammography or supersede it to become the standard for breast imaging. DBT involves taking multiple images in order to synthesize a new image that represents a slice through the breast volume -- hence the term tomosynthesis. The primary advantage of this paradigm is that it can reduce the amount of overlapping anatomy in the data, leading to improved visualization of potentially-cancerous findings. The difficulty in DBT is quantifying the advantages of the technology and determining the optimal conditions for its clinical use. This dissertation describes a virtual trial framework for assessing and optimizing DBT technology for the specific task of detecting small, low-contrast masses in the breast. It addresses each component of the imaging chain to some degree, from the patients/phantoms to the imaging hardware to the model observers used to measure signal detectability. The main focus, however, is on quantifying tradeoffs between three key parameters that affect image quality: (1) scan angle, (2) number of projections, and (3) exposure. We show that in low-density breast phantoms, detectability generally increases with both scan angle and number of projections in the anatomical-variability-limited (high-exposure) regime. We also investigate how breast density affects the optimal DBT scan parameters. We show task-specific results that support using an adaptive paradigm in DBT, where the imaging system reconfigures itself in response to information about the patient's breast density. The virtual framework described in this dissertation provides a platform for further investigations of image quality in 3D breast imaging.