Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.

扩散磁共振成像是一种研究大脑微观结构的非侵入性技术。如果使用适当的采集方法，则可以在信号中表示组织微观结构特性的差异，包括尺寸和各向异性。然而，为了描述潜在的特性，在设计采集协议时必须特别小心，因为程序中的任何变化都可能影响定量测量。这项工作回顾了使用扩散 MRI 研究大脑微观结构的最先进方法及其对微观结构差异和各种实验因素的敏感性。通过建模，可以将微米级组织的微观结构特性与毫米级扩散信号联系起来。在本文中，我们首先介绍扩散 MRI 和不同的编码方案。然后，简要解释了基于信号表示的方法和多室模型。综述了弥散 MRI 信号对微观结构成分的敏感性以及轴突轨迹的弯曲度对弥散信号的影响。然后审查影响导出指标的质量（准确度和精确度）的因素，包括随机噪声的影响以及采集参数的变化（即采样信号的数量、 b值和采集壳的数量）。最后但重要的是，描述了处理实验因素的典型方法，包括公正的措施和协调。我们以有关该主题的一些未来方向和建议来结束审查。