The sensitivity of diffusion MRI to microstructural properties and experimental factors.
The sensitivity of diffusion MRI to microstructural properties and experimental factors.
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DOI：
10.1016/j.jneumeth.2020.108951
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发表时间：
2021-01-01
影响因子：
3
通讯作者：
Jones DK
中科院分区：
文献类型：
--
作者：
Afzali M;Pieciak T;Newman S;Garyfallidis E;Özarslan E;Cheng H;Jones DK
本文综述了利用dMRI研究脑微结构的不同方法。微观结构的差异和实验因素的敏感性进行了研究。解释了基于信号表示的方法和多室模型。弥散磁共振成像是一种非侵入性的技术来研究大脑的微观结构。如果使用适当的采集方法，则可以在信号中表示组织的微观结构特性的差异，包括尺寸和各向异性。然而，为了描述基本属性，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。这项工作回顾了国家的最先进的方法，用于研究大脑的微观结构，使用扩散MRI和它们的敏感性，微观结构的差异和各种实验因素。使用建模，可以将微米尺度的组织的微观结构特性与毫米尺度的扩散信号相关联。在本文中，我们首先给出了一个介绍扩散MRI和不同的编码方案。然后，简要说明基于信号表示的方法和多室模型。扩散MRI信号的微结构成分的敏感性和轴突的轨迹上的扩散信号的弯曲度的影响进行了审查。然后审查影响导出度量的质量（准确度和精确度）的因素，包括随机噪声的影响和采集参数的变化（即，采样信号的数量、b值和采集外壳的数量）。最后，但重要的是，典型的方法来处理实验因素的描述，包括无偏的措施和协调。最后，我们对这一主题提出了一些未来的方向和建议。
This work reviews different methods for studying brain microstructure using dMRI. Sensitivity to microstructural differences and experimental factors is investigated. Signal representation-based methods and multi-compartment models are explained. 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.
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