Mapping complex cell morphology in the grey matter with double diffusion encoding MR: a simulation study

TL;DR

This simulation study demonstrates that advanced double diffusion encoding MRI techniques can non-invasively characterize complex cell morphology in grey matter, including soma size and branching patterns, which were previously difficult to assess.

Contribution

The paper introduces a simulation framework showing DDE MRI's sensitivity to cellular projection branching, a novel insight for brain tissue characterization.

Findings

Soma size affects diffusion signal dependence on b-value and time.

Branching order influences DDE signal angular modulation and microscopic anisotropy.

DDE can detect cellular projection branching, enabling non-invasive grey matter morphology assessment.

Abstract

This paper investigates the impact of cell body (soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to study the ability of dMRI/dMRS to characterize the complex morphology of brain grey matter, focusing on these two distinctive features. To this end, we employ a recently developed framework to create realistic meshes for Monte Carlo simulations, covering a wide range of soma sizes and branching orders of cellular projections, for diffusivities reflecting both water and metabolites. For SDE sequences, we assess the impact of soma size and branching order on the signal b-value dependence as well as the time dependence of the apparent diffusion coefficient (ADC). For DDE sequences, we assess their impact on the mixing time dependence of the signal angular modulation and of the estimated microscopic anisotropy, a promising contrast derived from DDE measurements. The SDE results show that soma size has a measurable impact on both the b-value and diffusion time dependence, for both water and metabolites. On the other hand, branching order has little impact on either, especially for water. In contrast, the DDE results show that soma size has a measurable impact on the signal angular modulation at short mixing times and the branching order significantly impacts the mixing time dependence of the signal angular modulation as well as of the derived microscopic anisotropy, for both water and metabolites. Our results confirm that soma size can be estimated from SDE based techniques, and most importantly, show for the first time that DDE measurements show sensitivity to the branching of cellular projections, paving the way for non-invasive characterization of grey matter morphology.