\hspace{2mm} Diffusion-weighted magnetic resonance imaging (dMRI) of the brain offers unique capabilities including noninvasive probing of tissue microstructure and structural connectivity. It is widely used for clinical assessment of disease and injury, and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements may suffer from strong noise and artifacts, and may exhibit high inter-session and inter-scanner variability in the data, as well as inter-subject heterogeneity in brain structure. Moreover, the relationship between measurements and the phenomena of interest can be highly complex. Recent years have witnessed increasing use of machine learning methods for dMRI analysis. This manuscript aims to assess these efforts, with a focus on methods that have addressed data preprocessing and harmonization, microstructure mapping, tractography, and white matter tract analysis. We study the main findings, strengths, and weaknesses of the existing methods and suggest topics for future research. We find that machine learning may be exceptionally suited to tackle some of the difficult tasks in dMRI analysis. However, for this to happen, several shortcomings of existing methods and critical unresolved issues need to be addressed. There is a pressing need to improve evaluation practices, to increase the availability of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.

翻译：\hspace{2mm} 脑部扩散加权磁共振成像（dMRI）具备独特的能力，包括对组织微观结构和结构连接性的无创探查。它被广泛应用于疾病与损伤的临床评估以及神经科学研究。分析dMRI数据以提取对医学和科学目的有用的信息可能具有挑战性。dMRI测量可能受到强噪声和伪影的影响，数据中可能表现出较高的会话间和扫描仪间变异性，以及脑结构在受试者间的异质性。此外，测量值与感兴趣现象之间的关系可能极为复杂。近年来，机器学习方法在dMRI分析中的应用日益增多。本文旨在评估这些研究工作，重点关注那些处理数据预处理与协调、微观结构映射、纤维束成像以及白质纤维束分析的方法。我们研究了现有方法的主要发现、优势和不足，并提出了未来研究的主题。我们发现，机器学习可能特别适合应对dMRI分析中的一些困难任务。然而，要实现这一点，需要解决现有方法的若干缺陷以及关键未决问题。迫切需要改进评估实践，增加丰富训练数据集和验证基准的可用性，并关注模型泛化能力、可靠性及可解释性等问题。