A review of diffusion tensor magnetic resonance imaging computational methods and software tools.

Area of Science:

• Neuroimaging
• Medical Physics
• Computational Biology

Background:

• Diffusion-weighted magnetic resonance imaging (dMRI) is crucial for non-invasively probing tissue microstructure.
• Diffusion Tensor Imaging (DTI) is a widely adopted dMRI technique for analyzing water diffusion patterns.
• Computational tools are essential for processing and analyzing complex dMRI datasets.

Purpose of the Study:

• To provide an updated review of computational methods in MRI, specifically for diffusion-weighted data.
• To summarize acquisition, modeling, and analysis techniques for dMRI, with a focus on DTI.
• To identify and list freely available software packages for dMRI data analysis.

Main Methods:

• Review of established and emerging computational approaches for dMRI data.
• Detailed discussion of preprocessing, processing, and post-processing steps in DTI analysis.
• Compilation and categorization of open-source software tools for diffusion MRI analysis.

Main Results:

• An overview of key computational strategies for diffusion MRI is presented.
• Commonly used DTI modeling and analysis pipelines are described.
• A curated list of accessible software solutions for researchers is provided.

Conclusions:

• Computational MRI, particularly DTI, offers powerful insights into tissue properties.
• Standardized processing and analysis pipelines are vital for reproducible dMRI research.
• Availability of free software lowers barriers to entry for diffusion MRI data analysis.