Diffusion-weighted magnetic resonance imaging (dMRI) offers unique
capabilities such as noninvasive assessment of brain's micro-structure and
structural connectivity. However, analyzing the dMRI data to extract useful
information for clinical and scientific purposes is challenging. The dMRI
measurements often suffer from strong noise and artifacts, there is usually
high inter-session and inter-scanner heterogeneity in the data and considerable
inter-subject variability in brain structure, and 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 micro-structure mapping, tractography, white matter tract analysis,
as well as data preprocessing and harmonization. We summarize 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. These include deficient evaluation practices, lack of
rich training datasets and validation benchmarks, as well as model
generalizability, reliability, and explainability concerns.

扩散加权磁共振成像（dMRI）提供了非侵入性评估大脑微结构和结构连接的独特能力。然而，分析 dMRI 数据以提取临床和科学目的的有用信息具有挑战性，而机器学习方法在解决 dMRI 分析中的困难任务方面可能具有潜力。然而，为了实现这一点，需要解决现有方法的缺陷和重要未解决问题，包括评估实践不足、缺乏丰富的训练数据集和验证基准以及模型的普适性、可靠性和解释性方面的问题。