New modeling and experimental framework to characterize hindered and restricted water diffusion in brain white matter

TLDR

The study proposes a framework combining hindered and restricted diffusion models (CHARMED) with diffusion tensor and q‑space MRI to characterize anisotropic water diffusion in brain white matter. The framework models white‑matter diffusion with a hindered extra‑axonal compartment described by an effective diffusion tensor and an intra‑axonal restricted cylinder compartment, using low‑b Gaussian and high‑b non‑Gaussian signal attenuation to estimate microstructural parameters such as fiber orientation, volume fractions, and diffusivities, and derives a 3‑D probability distribution via Fourier transform, validated on synthetic phantoms and excised spinal cord tissue. The framework demonstrates improved precision and accuracy in estimating multiple fiber orientations compared to conventional diffusion tensor imaging.

Abstract

To characterize anisotropic water diffusion in brain white matter, a theoretical framework is proposed that combines hindered and restricted models of water diffusion (CHARMED) and an experimental methodology that embodies features of diffusion tensor and q-space MRI. This model contains a hindered extra-axonal compartment, whose diffusion properties are characterized by an effective diffusion tensor, and an intra-axonal compartment, whose diffusion properties are characterized by a restricted model of diffusion within cylinders. The hindered model primarily explains the Gaussian signal attenuation observed at low b values; the restricted non-Gaussian model does so at high b. Both high and low b data obtained along different directions are required to estimate various microstructural parameters of the composite model, such as the nerve fiber orientation(s), the T2-weighted extra- and intra-axonal volume fractions, and principal diffusivities. The proposed model provides a description of restricted diffusion in 3D given by a 3D probability distribution (average propagator), which is obtained by 3D Fourier transformation of the estimated signal attenuation profile. The new model is tested using synthetic phantoms and validated on excised spinal cord tissue. This framework shows promise in determining the orientations of two or more fiber compartments more precisely and accurately than with diffusion tensor imaging.