Towards Mesoscopic Human Brain Imaging Using Non-Parametric Diffusion Tensor Distribution (DTD) MRI.

Area of Science:

• Neuroimaging
• Biophysics
• Diffusion MRI

Background:

• Magnetic Resonance (MR)-based brain imaging typically provides macroscopic information, averaging over large voxels.
• Subvoxel tissue composition is heterogeneous, containing distinct water pools at various length scales.
• Current methods lack robust techniques to quantify tissue water dynamics at these subvoxel scales.

Purpose of the Study:

• To develop a novel method for estimating the diffusion tensor distribution (DTD) in the human brain in vivo.
• To probe subvoxel tissue microstructure and water dynamics.
• To reveal hidden mesoscopic and microscopic features in the brain.

Main Methods:

• Developed an empirical spectroscopic diffusion MRI method using multi-dimensional Inverse Laplace Transform (ILT).
• Overcame ILT challenges by using a hierarchy of marginal distributions from single, double, and triple pulsed-field gradient (PFG) experiments.
• Validated the framework with simulated data and tested in vivo in healthy human subjects.

Main Results:

• Successfully reconstructed and segmented the DTD within voxels to identify different tissue/cell types and water pools.
• Removed confounding free water compartments and visualized intrinsic mesoscopic features.
• Computed DTI-derived quantitative imaging biomarkers from the DTD, revealing previously unobserved microstructural features.

Conclusions:

• The novel DTD MRI method provides unprecedented subvoxel resolution of brain microstructure in vivo.
• This technique has significant potential for understanding brain development, neurological disorders, and trauma.
• DTD MRI offers a more comprehensive view of brain tissue compared to conventional DTI.