Graduate School of Information Sciences, Hiroshima City University.
Magn Reson Med Sci. 2022 Mar 1;21(1):132-147. doi: 10.2463/mrms.rev.2021-0005. Epub 2021 May 21.
In this paper, fundamentals and recent progress for obtaining biological features quantitatively by using diffusion MRI are reviewed. First, a brief description of diffusion MRI history, application, and development was presented. Then, well-known parametric models including diffusion tensor imaging (DTI), diffusional kurtosis imaging (DKI), and neurite orientation dispersion diffusion imaging (NODDI) are introduced with several classifications in various viewpoints with other modeling schemes. In addition, this review covers mathematical generalization and examples of methodologies for the model parameter inference from conventional fitting to recent machine learning approaches, which is called Q-space learning (QSL). Finally, future perspectives on diffusion MRI parameter inference are discussed with the aspects of imaging modeling and simulation.
本文综述了利用扩散 MRI 定量获取生物特征的基本原理和最新进展。首先，简要介绍了扩散 MRI 的历史、应用和发展。然后，介绍了著名的参数模型，包括扩散张量成像（DTI）、扩散峰度成像（DKI）和神经纤维方向分散扩散成像（NODDI），并从不同的角度对其他建模方案进行了分类。此外，本综述还涵盖了从传统拟合到最近的机器学习方法的模型参数推断的数学推广和方法示例，这被称为 Q 空间学习（QSL）。最后，讨论了扩散 MRI 参数推断的未来展望，包括成像建模和模拟方面。
