Title: Diffusion MRI with Machine Learning Show Chinese title

Title: 弥散磁共振成像与机器学习

Abstract: \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. Show Chinese abstract

Abstract: \hspace{2mm} 脑部扩散加权磁共振成像（dMRI）提供了独特的功能，包括无创探测组织微观结构和结构连通性。 它广泛用于临床疾病和损伤的评估，以及神经科学研究。 分析dMRI数据以提取用于医学和科学研究的有用信息可能具有挑战性。 dMRI测量可能会受到强烈噪声和伪影的影响，并且在数据中可能表现出高会话间和扫描仪间变异性，以及脑结构的受试者间异质性。 此外，测量与感兴趣的现象之间的关系可能是高度复杂的。 近年来，机器学习方法在dMRI分析中的使用越来越多。 本手稿旨在评估这些努力，重点关注那些已经解决了数据预处理和标准化、微观结构映射、弥散张量追踪和白质束分析的方法。 我们研究了现有方法的主要发现、优势和劣势，并提出了未来研究的主题。 我们发现，机器学习可能非常适合解决dMRI分析中的一些难题。 然而，为了实现这一点，需要解决现有方法的一些不足和关键未解决的问题。 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，以及关注模型的泛化能力、可靠性和可解释性。