Diffusion MRI with Machine Learning

Davood Karimi

Published 2024-01-01, updated 2024-07-26Version 2

Diffusion-weighted magnetic resonance imaging (dMRI) offers unique capabilities including noninvasive probing of brain's tissue microstructure and structural connectivity. It is widely used for clinical assessment of brain pathologies and for neuroscience research. Analyzing the dMRI data to extract useful information for medical and scientific purposes can be challenging. The dMRI measurements often suffer from strong noise and artifacts, there is usually high inter-session and inter-scanner variability in the data, and considerable 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. These include deficient evaluation practices, lack of rich training datasets and validation benchmarks, as well as model generalizability, reliability, and explainability concerns.