SPECT RECONSTRUCTION ALGORITHMS AND PARALLEL COMPUTING

此应用程序用于研究图像重建算法 以及用于核医学和其他领域的并行计算硬件 医学成像技术。还包括对新方法的调查 评估和预测图像质量。中国的长期目标是 算法研究是在定量准确性方面的改进 重建的图像和改进的诊断性能 观察者使用这些图像。硬件组件的目标是 研究目的是生产一种经济的计算机系统，非常适合图像 重建，性能接近超级计算机。这个 图像质量研究的目的是开发来自 成像系统的临床有意义的品质因数可能是 派生的。 算法研究将研究贝叶斯重建， 数据模型的不同形式(精确泊松或最小二乘 近似)和关于对象的不同形式的先验信息 正在被重建。分散补偿的新途径 将研究SPECT中的辐射和衰减，以及新的方法 将开发感兴趣区域量化。 将为现有的并行计算机(TRIMM)开发软件， 允许对在该授权上开发的算法进行详细探索， 以及更传统的算法。升级后的并行计算机， 具有1-2个GFLOPS能力，也将设计和建造。 图像质量的优值系数是基于人类或 在现实的、临床相关的任务上的模范观察员。特例 重点将放在最佳线性观测器上，但不是非线性观测器 模特也将接受调查。模型观察员的使用将是 得到了广泛的心理物理研究的证实。当有效模型为 一旦发现，它们将被用来解决许多长期存在的形象问题 重构，包括最佳停止点和正则化 迭代算法中的函数及不同形式先验的影响 信息。

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.