Abstract
Probing microstructure with diffusion magnetic resonance imaging (dMRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in the tissue. The vast majority of these biophysical models of diffusion in white matter assume that the measured dMRI signal is the sum of the signals emanating from each of the constituent compartments, each of which exhibits a distinct behaviour in the b-value and/or orientation domain. Many of these models further assume that the dMRI behaviour of the oriented compartments (e.g. the intra-axonal space) is identical between distinct fibre populations, at least at the level of a single voxel. This implicitly assumes that any potential biological differences between fibre populations are negligible, at least as far as is measurable using dMRI. Here, we validate this assumption by means of a voxel-wise, model-free signal decomposition that, under the assumption above and in the absence of noise, is shown to be rank-1. We evaluate the effect size of signal components beyond this rank-1 representation and use permutation testing to assess their significance. We conclude that in the healthy adult brain, the dMRI signal is adequately represented by a rank-1 model, implying that biologically more realistic, but mathematically more complex fascicle-specific microstructure models do not capture statistically significant or anatomically meaningful structure, even in extended high-b diffusion MRI scans.
Highlights
Probing tissue microstructure with in vivo magnetic resonance imaging (MRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in intra- and extracellular space
This shared orientation structure can be captured in a single orientation distribution P, and the microstructural characteristics can be modelled by a single voxel-level kernel H, whose signal response is the mixture of the constituent compartment responses
This paper has introduced a model-independent means of evaluating the single convolution assumption implicit to most biophysical models for white matter diffusion MRI, i.e., the assumption that the signal is sufficiently described with a single orientation distribution function (ODF) and a single rotation-invariant response function per voxel
Summary
Probing tissue microstructure with in vivo magnetic resonance imaging (MRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in intra- and extracellular space. Many models further impose the constraint that the various compartments in a single voxel share the same orientation structure; for example, the extra-cellular space may be modelled as a distinct compartment with its own signal characteristics, but oriented identically to the intra-axonal compartment. Under this assumption, this shared orientation structure can be captured in a single orientation distribution P , and the microstructural characteristics can be modelled by a single voxel-level kernel H , whose signal response is the mixture of the constituent compartment responses
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