Department of Nuclear Medicine and Medical Imaging Research Center, KU Leuven, Leuven, Belgium.
Phys Med Biol. 2013 Jun 21;58(12):R63-96. doi: 10.1088/0031-9155/58/12/R63. Epub 2013 Jun 5.
There is an increasing interest in iterative reconstruction (IR) as a key tool to improve quality and increase applicability of x-ray CT imaging. IR has the ability to significantly reduce patient dose; it provides the flexibility to reconstruct images from arbitrary x-ray system geometries and allows one to include detailed models of photon transport and detection physics to accurately correct for a wide variety of image degrading effects. This paper reviews discretization issues and modelling of finite spatial resolution, Compton scatter in the scanned object, data noise and the energy spectrum. The widespread implementation of IR with a highly accurate model-based correction, however, still requires significant effort. In addition, new hardware will provide new opportunities and challenges to improve CT with new modelling.
人们对迭代重建(IR)越来越感兴趣,将其视为提高 X 射线 CT 成像质量和适用性的关键工具。IR 具有显著降低患者剂量的能力;它提供了从任意 X 射线系统几何形状重建图像的灵活性,并允许包括光子传输和检测物理的详细模型,以准确校正各种图像降质效应。本文综述了离散化问题和有限空间分辨率、扫描物体中的康普顿散射、数据噪声和能谱的建模。然而,广泛采用具有高度精确基于模型校正的 IR 仍然需要大量的努力。此外,新的硬件将为改进 CT 提供新的机会和挑战,并引入新的建模方法。