SPECT RECONSTRUCTION ALGORITHMS AND PARALLEL COMPUTING

此应用程序用于研究图像重建算法

This application is for investigation of image-reconstruction algorithms and parallel-computing hardware for use in nuclear medicine and other areas of medical imaging. Also included is investigation of new ways of assessing and predicting image quality. The long-term goals of the algorithmic research are improvements in quantitative accuracy in the reconstructed images and improved diagnostic performance when human observers use the images. The objective of the hardware component of the research is to produce an economical computer system, well suited to image reconstruction, with a performance approaching that a supercomputers. The image-quality studies are aimed at development of model observers from which clinically meaningful figures of merit for imaging systems may be derived. The algorithmic studies will investigate Bayesian reconstruction, with different forms for the data model (exact Poisson or least-squares approximations) and different forms of prior information about the object being reconstructed. New approaches to compensation for scattered radiation and attenuation in SPECT will be studied, and new methods for region-of-interest quantitation will be developed. Software for the existing parallel computer (TRIMM) will be developed, allowing a detailed exploration of the algorithms developed on this grant, as well as more conventional algorithms. An upgraded parallel computer, with 1-2 GFLOPS capability, will also be designed and constructed. The figures of merit for image quality are based on performance of human or model observers on realistic, clinically relevant tasks. Particular emphasis will be placed on the optimum linear observer, but nonlinear models will also be investigated. The use of model observers will be validated by extensive psychophysical studies. When valid models are found, they will be used to address many long-standing problems in image reconstruction, including the optimum stopping point and regularizing function in iterative algorithm and the effects of various forms of prior information.