Speaker
Description
Axon radius and extra-axonal volume fraction are fundamental descriptors of white matter microstructure, critically shaping conduction velocity and the efficiency of neural communication. Although existing diffusion MRI models have attempted to estimate these parameters, their limited sensitivity and constrained assumptions often result in unstable and biased estimates. Such bias typically arises from inappropriate compartmental modelling that can not capture the underlying biophysical processes accurately, and from confounding effects among the fitted model parameters. To overcome these limitations, we propose a novel sub-diffusion compartmental model that achieves accurate estimation of axon radius and extra-axonal volume fraction in axon substrates with both uniform and gamma-distributed radii. Using numerical simulations of white matter microstructure with varying axon radii, diffusion times, and noise levels, we demonstrate that the proposed model yields improved stability of extracellular volume fraction and axon radius estimates compared to a mono-exponential extracellular diffusion model. These results suggest that incorporating sub-diffusive extracellular dynamics can mitigate diffusion time-dependent bias in compartmental DW-MRI modelling.
