Assessing and Optimizing Pinhole SPECT Imaging Systems for Detection Tasks

The subject of this dissertation is the assessment and optimization of image quality for multiple-pinhole, multiple-camera SPECT systems. These systems collect gamma-ray photons emitted from an object using pinhole apertures. Conventional measures of image quality, such as the signal-to-noise ratio or the modulation transfer function, do not predict how well a system's images can be used to perform a relevant task. This dissertation takes the stance that the ultimate measure of image quality is to measure how well images produced from a system can be used to perform a task. Furthermore, we recognize that image quality is inherently a statistical concept that must be assessed for the average task performance across a large ensemble of images.The tasks considered in this dissertation are detection tasks. Namely we consider detecting a known three-dimensional signal embedded in a three-dimensional stochastic object using the Bayesian ideal observer. Out of all possible observers (human or otherwise) the ideal observer sets the absolute upper bound for detection task performance by using all possible information in the image data. By employing a stochastic object model we can account for the effects of object variability, which has a large effect on observer performance.An imaging system whose hardware has been optimized for ideal observer detection task performance is an imaging system that maximally transfers detection task relevant information to the image data.The theory and simulation of image quality, detection tasks, and gamma-ray imaging are presented. Assessments of ideal observer detection task performance are used to optimize imaging hardware for SPECT systems as well as to rank different imaging system designs