Resolving bundle-specific intra-axonal T₂ values within a voxel using diffusion-relaxation tract-based estimation.

Area of Science:

• Neuroimaging
• Biophysics
• Computational Neuroscience

Background:

• Most MRI voxels contain multiple white matter fiber populations, complicating microstructural analysis.
• Resolving intra-voxel T2 heterogeneity is crucial but remains an open challenge in neuroimaging.
• Existing methods often assume single fiber populations or uniform T2 properties within voxels.

Purpose of the Study:

• To introduce COMMIT-T2, a novel framework for estimating multiple intra-axonal T2 values within individual voxels.
• To leverage tractography-based spatial regularization with diffusion-relaxometry data for improved T2 mapping.
• To overcome limitations of current voxel-based T2 estimation methods in complex white matter regions.

Main Methods:

• Developed the COMMIT-T2 framework integrating tractography and diffusion-relaxometry.
• Utilized tract-specific streamline counts to determine the number of recoverable T2 values per voxel.
• Validated the approach using in silico and in vivo human brain MRI data.

Main Results:

• Successfully recovered distinct tract-specific T2 profiles, even in complex three-way crossing regions.
• Demonstrated superior performance of COMMIT-T2 compared to voxelwise T2 mapping methods.
• Showcased the ability to resolve intra-voxel T2 heterogeneity for individual fiber populations.

Conclusions:

• COMMIT-T2 offers a robust solution for mapping intra-axonal T2 heterogeneity in the human brain.
• This method enhances the characterization of white matter microstructure by resolving distinct fiber populations.
• COMMIT-T2 represents a significant advancement for diffusion-relaxometry and neuroimaging analysis.