Optical modeling, design optimization, and performance analysis of a gamma camera for detection of breast cancer
AuthorSain, John David
Health Sciences, Radiology.
Health Sciences, Oncology.
AdvisorBarrett, Harrison H.
MetadataShow full item record
PublisherThe University of Arizona.
RightsCopyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction or presentation (such as public display or performance) of protected items is prohibited except with permission of the author.
AbstractThis dissertation presents the research performed to develop an optical model, improve some design parameters, and analyze the performance of the UA modular gamma camera. Initially we provide a brief background on nuclear medical imaging with scintillation cameras. The key hardware components of a camera are introduced, and some of the fundamental physics involved in the detection of gamma rays is explained. Then we describe a stand-alone modular camera imaging system that was developed to image human breasts in the clinic. The hardware and software components, calibration procedure, and general operation of the system are detailed. We explain the concepts of position estimation and scatter rejection and note how they have been applied to imaging with the UA modular gamma camera. Position estimation uses the output signals of the camera to determine where an incident gamma ray interacted within the camera, and scatter rejection uses the signals to decide whether or not an incident gamma ray underwent scattering prior to being detected by the camera. Then we present an analytical optical model of the UA modular gamma camera. Taking into account physical and optical properties of the camera components, the model performs radiometric calculations to estimate the mean response of the camera to a scintillation event anywhere within the scintillation crystal. The results of several studies using the optical model to test and improve some camera design parameters are reported. Finally, we demonstrate how straightforward signal detection theory can be used to evaluate the performance of a modular gamma camera for the task of detecting signals in noisy backgrounds. Guided by the preliminary design of a dedicated breast imaging system, estimates of how well the UA modular gamma camera can detect lesions within human breasts were generated.
Degree ProgramGraduate College