Abstract
PurposeIn the brain, there is growing interest in using the temporal diffusion spectrum to characterize axonal geometry in white matter because of the potential to be more sensitive to small pores compared to conventional time-dependent diffusion. However, analytical expressions for the diffusion spectrum of particles have only been derived for simple, restricting geometries such as cylinders, which are often used as a model for intra-axonal diffusion. The extra-axonal space is more complex, but the diffusion spectrum has largely not been modeled. We propose a model for the extra-axonal space, which can be used for interpretation of experimental data.Theory and MethodsAn empirical model describing the extra-axonal space diffusion spectrum was compared with simulated spectra. Spectra were simulated using Monte Carlo methods for idealized, regularly and randomly packed axons over a wide range of packing densities and spatial scales. The model parameters are related to the microstructural properties of tortuosity, axonal radius, and separation for regularly packed axons and pore size for randomly packed axons.ResultsForward model predictions closely matched simulations. The model fitted the simulated spectra well and accurately estimated microstructural properties.ConclusionsThis simple model provides expressions that relate the diffusion spectrum to biologically relevant microstructural properties. Magn Reson Med 73:2306–2320, 2015. © 2014 The authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.
Highlights
Diffusion-weighted magnetic resonance imaging has enormous potential to noninvasively estimate the geometric properties of biological tissues
The goal of the current work is to propose a model for the diffusion spectrum in the extra-axonal space (EAS) with improved accuracy that may yield white matter microstructural information
We present an analytical model for the diffusion spectrum of the EAS that is applicable to square, hexagonally, and randomly packed cylinders
Summary
Diffusion-weighted magnetic resonance imaging has enormous potential to noninvasively estimate the geometric properties of biological tissues. This remarkable capability is grounded in the sensitivity of the natural motion of water molecules to their microenvironment, which imparts a characteristic change to water diffusion. This altered diffusion provides a signature of the size and shape of the compartments to which it is confined (1–3). In the context of biological tissues, many of these geometric properties relate to tissue function and health (11), making such a technique of great interest as a biomarker
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