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Temporal Diffusion Ratio (TDR) for imaging restricted diffusion: Optimisation and pre-clinical demonstration.

William Warner¹, Marco Palombo², Renata Cruz³

¹Centre for Medical Image Computing (CMIC), Computer Science Department, University College London, United Kingdom.

|February 7, 2023

Summary

This summary is machine-generated.

Temporal Diffusion Ratio (TDR) offers a new way to measure restricted diffusion and pore sizes using standard MRI sequences. Optimized TDR improves contrast and correlates with axon diameter, showing promise beyond model-based methods.

Keywords:

Axon diameter Diffusion Microstructure Optimization Spinal cord TDR

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Area of Science:

Neuroimaging
Diffusion MRI
Biophysics

Background:

Diffusion MRI (dMRI) is crucial for characterizing tissue microstructure.
Existing methods often rely on complex models and assumptions.
Temporal Diffusion Ratio (TDR) is a novel dMRI technique offering model-free contrast for restricted diffusion.

Purpose of the Study:

To introduce and optimize the Temporal Diffusion Ratio (TDR) method for dMRI.
To evaluate TDR's performance in simulations and pre-clinical experiments.
To assess TDR's potential for informing on pore sizes and cellular structures.

Main Methods:

Simulated dMRI data with varying tissue microstructures (cylinders, spheres).
Optimization of TDR acquisition parameters (gradient duration, diffusion time, gradient strength).
Experimental validation using rat spinal cord dMRI and histology.

Main Results:

Optimized TDR, contrasting short/high-strength gradients with long/low-strength gradients, maximizes contrast.
Using a subset of diffusion gradients improves TDR contrast in Rician noise.
Optimized TDR showed improved contrast in rat spinal cord and strong correlation with axon diameter.

Conclusions:

Optimized TDR is a promising, model-free dMRI technique for assessing pore sizes and restricted diffusion.
TDR offers advantages over traditional model-based approaches.
TDR has potential as a complementary or alternative method in neuroimaging.