Ravishankar Saiprasad, Ye Jong Chul, Fessler Jeffrey A
Departments of Computational Mathematics, Science and Engineering, and Biomedical Engineering at Michigan State University, East Lansing, MI, 48824 USA.
Department of Bio and Brain Engineering and Department of Mathematical Sciences at the Korea Advanced Institute of Science & Technology (KAIST), Daejeon, South Korea.
Proc IEEE Inst Electr Electron Eng. 2020 Jan;108(1):86-109. doi: 10.1109/JPROC.2019.2936204. Epub 2019 Sep 19.
The field of medical image reconstruction has seen roughly four types of methods. The first type tended to be analytical methods, such as filtered back-projection (FBP) for X-ray computed tomography (CT) and the inverse Fourier transform for magnetic resonance imaging (MRI), based on simple mathematical models for the imaging systems. These methods are typically fast, but have suboptimal properties such as poor resolution-noise trade-off for CT. A second type is iterative reconstruction methods based on more complete models for the imaging system physics and, where appropriate, models for the sensor statistics. These iterative methods improved image quality by reducing noise and artifacts. The FDA-approved methods among these have been based on relatively simple regularization models. A third type of methods has been designed to accommodate modified data acquisition methods, such as reduced sampling in MRI and CT to reduce scan time or radiation dose. These methods typically involve mathematical image models involving assumptions such as or . A fourth type of methods replaces mathematically designed models of signals and systems with or models inspired by the field of . This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.
医学图像重建领域大致出现了四种类型的方法。第一种类型往往是解析方法，例如用于X射线计算机断层扫描（CT）的滤波反投影（FBP）以及用于磁共振成像（MRI）的傅里叶逆变换，这些方法基于成像系统的简单数学模型。这些方法通常速度很快，但具有次优特性，例如CT的分辨率与噪声权衡较差。第二种类型是迭代重建方法，基于成像系统物理的更完整模型，并在适当情况下基于传感器统计模型。这些迭代方法通过减少噪声和伪影来提高图像质量。其中获得美国食品药品监督管理局（FDA）批准的方法基于相对简单的正则化模型。第三种类型的方法旨在适应修改后的数据采集方法，例如在MRI和CT中减少采样以减少扫描时间或辐射剂量。这些方法通常涉及数学图像模型，包含诸如 或 之类的假设。第四种类型的方法用受 领域启发的 或 模型取代信号和系统的数学设计模型。本文重点关注医学图像重建中的两个最新趋势：基于稀疏性或低秩模型的方法，以及基于机器学习技术的数据驱动方法。
