One-Step Optimization of Adaptive SPECT Systems

Abstract

In this dissertation a method for one-step optimization of an adaptive Single Photon Emission Computed Tomography (SPECT) system is presented. Adaptive imaging systems can quickly change their hardware configuration in response to data being generated in order to improve image quality. The approach to assessment of image quality is based on the usefulness of images for performing a given task. The performance is measured by calculating a scalar quantity known as the figure of merit. The optimization algorithm, which aims at finding the optimal figure of merit, could either alter the system continuously during acquisition, or it could apply the one-step adaptation method presented by Barrett et al. which is adopted in this work. Prior to the optimization, the adaptive SPECT system is modeled by a ray tracing module. The system matrices for a selection of imaging configurations are simulated and stored. Access to this information expedites the optimization process, however it limits the solution space to a discrete set of adaptations. Depending on the size of the solution space we utilize either the grid search or the genetic algorithm to find the optimum adaptation. The optimization strategy is to find the adaptation that maximizes the performance on a signal estimation task. To start with, a simulated object model containing a spherical signal is imaged with a scout configuration. A Markov-Chain Monte Carlo (MCMC) technique utilizes the scout data to generate an ensemble of possible objects consistent with the scout data. This object ensemble is imaged by numerous simulated hardware configurations and for each system estimates of signal activity, size and location are calculated via the Scanning Linear Estimator (SLE). A figure of merit, based on a Modified Dice Index (MDI), quantifies the performance of each imaging configuration. This figure of merit is calculated by multiplying two terms: the first term uses the definition of the Dice similarity index to determine the percent of overlap between the actual and the estimated spherical signal, the second term utilizes an exponential function that measures the squared error for the activity estimate. The MDI combines the error in estimates of activity, size, and location, in one convenient metric and it allows for simultaneous optimization of the SPECT system with respect to all the estimated signal parameters. The average MDI for the object ensemble is a scalar value that quantifies the performance of a particular imaging configuration. The results of our optimizations indicate that adaptive systems perform better than non-adaptive in conditions where the diagnostic scan has a low photon count which makes this method suitable for conducting dynamic studies. Furthermore, this method can be used as a tool to evaluate the impact of design trade offs prior to the construction of adaptive systems. Examples of design trade-off include fixing the number of projection angles or adding multiplexing capability to the pinhole apertures. The most important contribution, which makes all the subsequent optimization results and design analysis possible, is the parallel implementation of SLE that can compute a figure of merit for a single configuration in 30 to 60 seconds.