Title: Diffusion MRI with Machine Learning Show Chinese title

Title: 基于机器学习的扩散磁共振成像

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}弥散加权磁共振成像（dMRI）对大脑提供了独特的功能，包括对组织微结构和结构连接的无创探测。它被广泛用于疾病的临床评估和损伤分析，以及神经科学的研究。分析dMRI数据以提取用于医学和科学目的的信息可能具有挑战性。dMRI测量可能会受到强噪声和伪影的影响，并且在数据中可能表现出高会话间和扫描仪间的变异性，以及脑结构的个体间异质性。此外，测量与感兴趣现象之间的关系可能非常复杂。近年来，机器学习方法在dMRI分析中的使用日益增加。本文旨在评估这些努力，重点放在解决了数据预处理和标准化、微结构映射、纤维追踪和白质纤维束分析的方法上。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特别适合解决dMRI分析中的一些困难任务。然而，要做到这一点，需要解决现有方法的一些不足和关键未解决问题。迫切需要改进评估实践，提高丰富训练数据集和验证基准的可用性，以及模型的泛化能力、可靠性和可解释性问题。