Multidimensional diffusion MRI methods with confined subdomains

TL;DR

This paper introduces a confinement tensor model for multidimensional diffusion MRI that better captures restricted water diffusion in heterogeneous media, enhancing microstructural specificity in brain imaging.

Contribution

It proposes a new confinement tensor model that accounts for restricted diffusion and integrates it into existing multidimensional dMRI methods for improved microstructure analysis.

Findings

The confinement tensor model accurately captures signal modulations due to restricted diffusion.

The model can be incorporated into multidimensional dMRI methods for better microstructure characterization.

Application to human brain data demonstrates potential for enhanced microstructural insights.

Abstract

Diffusion Magnetic Resonance Imaging (dMRI) is an imaging technique with exquisite sensitivity to the microstructural properties of heterogeneous media. The conventionally adopted acquisition schemes involving single pulsed field gradients encode the random motion of water molecules into the NMR signal, however typically conflating the effects of different sources contributing to the water motion. Time-varying magnetic field gradients have recently been considered for disentangling such effects during the data encoding phase, opening to the possibility of adding specificity to the recovered information about the medium's microstructure. Such data is typically represented via a diffusion tensor distribution (DTD) model, thus assuming the existence of several non-exchanging compartments in each of which diffusion is unrestricted. In this work, we consider a model that takes confinement into account and possesses a diffusion time-dependence closer to that of restricted diffusion, to replace the free diffusion assumption in multidimensional diffusion MRI methods. We first demonstrate how the confinement tensor model captures the relevant signal modulations impressed by water diffusing in both free and closed spaces, for data simulated with a clinically feasible protocol involving time-varying magnetic field gradients. Then, we provide the basis for incorporating this model into two multidimensional dMRI methods, and attempt to recover a confinement tensor distribution (CTD) on a human brain dataset.