The effect of realistic geometries on the susceptibility-weighted MR signal in white matter

TL;DR

This study demonstrates that realistic axonal geometries significantly influence susceptibility-weighted MR signals in white matter, highlighting the importance of accurate microstructural modeling for interpreting MRI data related to demyelination.

Contribution

It introduces a more realistic three-compartment white matter model incorporating varied axonal geometries, improving the accuracy of MR signal predictions over traditional cylindrical assumptions.

Findings

Realistic geometries alter MR signal predictions.

Circular models differ significantly from realistic geometries.

Microstructural property estimates may be biased by assumed geometry.

Abstract

Purpose: To investigate the effect of realistic microstructural geometry on the susceptibility-weighted magnetic resonance (MR) signal in white matter (WM), with application to demyelination. Methods: Previous work has modeled susceptibility-weighted signals under the assumption that axons are cylindrical. In this work, we explore the implications of this assumption by considering the effect of more realistic geometries. A three-compartment WM model incorporating relevant properties based on literature was used to predict the MR signal. Myelinated axons were modeled with several cross-sectional geometries of increasing realism: nested circles, warped/elliptical circles and measured axonal geometries from electron micrographs. Signal simulations from the different microstructural geometries were compared to measured signals from a Cuprizone mouse model with varying degrees of demyelination. Results: Results from simulation suggest that axonal geometry affects the MR signal. Predictions with realistic models were significantly different compared to circular models under the same microstructural tissue properties, for simulations with and without diffusion. Conclusion: The geometry of axons affects the MR signal significantly. Literature estimates of myelin susceptibility, which are based on fitting biophysical models to the MR signal, are likely to be biased by the assumed geometry, as will any derived microstructural properties.