Diffusion MRI with Machine Learning.

Area of Science:

• Neuroimaging
• Biomedical Engineering
• Computational Neuroscience

Background:

• Diffusion-weighted magnetic resonance imaging (dMRI) is crucial for noninvasively studying brain microstructure and connectivity.
• dMRI data analysis is complex due to noise, artifacts, variability, and intricate relationships between measurements and biological phenomena.

Purpose of the Study:

• To assess the application of machine learning (ML) methods in dMRI analysis.
• To focus on ML for preprocessing, harmonization, microstructure mapping, tractography, and white matter tract analysis.
• To identify strengths, weaknesses, and future research directions for ML in dMRI.

Main Methods:

• Review and analysis of recent machine learning approaches applied to dMRI data.
• Evaluation of methods addressing data preprocessing, harmonization, microstructure mapping, and tractography.
• Assessment of existing literature on ML for white matter tract analysis.

Main Results:

• Machine learning methods show significant potential for addressing complex challenges in dMRI analysis.
• Existing ML methods have limitations concerning data quality, standardization, and evaluation practices.
• There is a need for improved datasets, validation benchmarks, and model generalizability.

Conclusions:

• Machine learning is well-suited for advancing dMRI analysis, particularly in microstructure and tractography.
• Addressing shortcomings in evaluation, data availability, and model reliability is critical for widespread adoption.
• Future research should focus on enhancing generalizability, reliability, and explainability of ML models in dMRI.