🤖 AI Summary
dMRI analysis faces core challenges including severe noise, high inter-scanner and inter-subject variability, and complex microstructural modeling. This study systematically reviews and empirically evaluates machine learning across the full dMRI pipeline—signal denoising, harmonization, microstructural mapping, fiber tractography, and white-matter pathway quantification—and establishes, for the first time, its applicability boundaries. We innovatively integrate CNNs, GANs, VAEs, transfer learning, multi-site harmonization, and explainable AI (XAI), while proposing a benchmark dataset construction and validation framework tailored for clinical deployment. Our analysis identifies shared bottlenecks in model robustness, reproducibility, and interpretability, and identifies generalizability and standardized evaluation as critical leverage points. The work provides a methodological guide and research roadmap for developing trustworthy, reproducible, and interpretable next-generation dMRI analysis tools.
📝 Abstract
Abstract 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 intersession and interscanner variability in the data, as well as intersubject 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.