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层析重建问题的例子，这些问题可以从改进的重建技术中受益匪浅。 本研究介绍了一种战略的发展，计算效率的重建算法，直接搜索的统计最佳拟合测量数据。 这种方法利用了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程序中的重建。 该研究有三个基本组成部分：1）提出了一个通用的测量系统模型，该模型描述了传输和发射问题的物理测量装置，并且可以扩展到包括散射等非线性效应。 2)一类新的计算易处理的图像截面模型被提出，保留图像细节，同时抑制噪声伪影。 3)一个快速的数值算法，被称为高斯-赛德尔，提出了高效和准确的多尺度重建方法的基础。 使用从劳伦斯利弗莫尔国家实验室的非破坏性评估部门收集的数据，正在对化学技术进行评估。

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.