Model Based Tomography: A Comprehensive Approach to Iterative Image Reconstruction

低剂量透射医学成像、发射医学成像和材料的无损检测都是断层成像重建问题的例子，这些问题可以从改进的重建技术中受益匪浅。本研究介绍了一种开发计算效率高的重建算法的策略，该算法直接搜索与测量数据的统计最优拟合。这种方法利用数字计算和存储的广泛可用性，大大提高了要求苛刻的应用中的重建。本研究包括三个基本组成部分：1)提出了一种通用的测量系统模型，该模型既具有传输和发射问题的物理测量装置的特征，又可以扩展到包括散射等非线性效应。2)提出了一种新的计算易于处理的图像截面模型，该模型在保留图像细节的同时抑制了噪声。3)提出了一种快速的数值算法Gauss-Seidel，作为高效、精确的多尺度重建方法的基础。算法技术正在使用从劳伦斯利弗莫尔国家实验室无损评估部门收集的数据进行评估。

Low dosage transmission medical imaging, emission medical imaging, and nondestructive testing of materials are all examples of tomographic reconstruction problems which can benefit greatly from improved reconstruction techniques. This research introduces a strategy for the development of computationally efficient reconstruction algorithms which directly search for the statistically optimal fit to measured data. This approach exploits the wide availability of digital computation and storage to substantially improve reconstruction in demanding applications. The research has three essential components: 1) A general measurement system model is being proposed which characterizes the physical measurement apparatus for both the transmission and emission problems, and can be extended to include nonlinear effects such as scattering. 2) A new class of computationally tractable image cross section models is being proposed which preserves image detail while suppressing noise artifacts. 3) A fast numerical algorithm, known as Gauss-Seidel, is proposed as a basis for efficient and accurate multiscale reconstruction methods. Algorithmic techniques are being evaluated using data collected from the Nondestructive Evaluation Section at the Lawrence Livermore National Laboratories.