ArticleMagnetic resonance in medicine2026
Investigating the Sensitivity of the Diffusion MRI Signal to Magnetization Transfer and Permeability via Monte-Carlo Simulations.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeMagnetization transfer (MT) and water exchange via permeability operate on a similar spatiotemporal scale to water diffusion. In this study, we use a simulation-based approach to characterize how MT and permeability impact (1) diffusion-weighted MRI (dMRI) measurements from cylindrical substrates and (2) parameter estimation using a two-compartment model of white matter.
methodsWe used Monte-Carlo simulations to model the dMRI signal inside and outside axons by simulating signals from parallel cylinders with different diameters and volume densities. We subsequently introduced membrane permeability and MT at the cylinder walls to investigate their impact on the dMRI signal. We fitted a two-compartment model to the simulated signal to produce estimates of the cylinder diameter and density. We evaluated the impact of MT and permeability by comparing the fitted diameter and density to the simulated ground truth.
resultsPermeability leads to underestimation (up to 100%) of cylinder diameter and density. Specifically, by enabling isochromats to escape from restrictions and diffuse more freely, permeability makes the overall displacement profile closer to the extra-axonal displacement profile. MT had limited effects on diameter estimation but caused substantial bias (20%-50%) in volume density estimates depending on the ratio of the intra-axonal and extra-axonal volume fraction. This is due to the intra-axonal and extra-axonal space having different surface-to-volume ratios and therefore different surface relaxation rates.
conclusionPermeability and MT can considerably influence the dMRI signal. They increase the relative contribution from larger cylinders to the dMRI signal and bias microstructural parameter estimates derived from dMRI data.
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