Diffusion time dependence of microstructural parameters in fixed spinal cord

Kurtosis
DOI: 10.1016/j.neuroimage.2017.08.039 Publication Date: 2017-08-14T23:04:35Z
ABSTRACT
30 pages, 9 figures, 1 table<br/>Biophysical modelling of diffusion MRI is necessary to provide specific microstructural tissue properties. However, estimating model parameters from data with limited diffusion gradient strength, such as clinical scanners, has proven unreliable due to a shallow optimization landscape. On the other hand, estimation of diffusion kurtosis (DKI) parameters is more robust as the clinical acquisitions typically probe a regime in which the associated 4th order cumulant expansion is adequate; however, its parameters are not microstructurally specific a priori. Given an appropriate biophysical model, its parameters may be connected to DKI parameters, but it was previously shown that at the DKI level, it still does not provide sufficient information to uniquely determine all model parameters. Earlier work has shown that by neglecting axonal dispersion, this parameter degeneracy reduces to the question of whether intra-axonal diffusivity is larger than or smaller than extra-axonal diffusivity. Here we develop a model of diffusion in spinal cord white matter including axonal dispersion and demonstrate stable estimation of all model parameters from DKI. By employing the recently developed fast axisymmetric DKI, we use stimulated echo acquisition mode to collect data over an unprecedented diffusion time range with very narrow diffusion gradient pulses, enabling finely resolved measurements of diffusion time dependence of both net diffusion and kurtosis metrics, as well as model intra- and extra-axonal diffusivities, and axonal dispersion. Our results demonstrate substantial time dependence of all parameters except volume fractions, and the additional time dimension provides support for intra-axonal diffusivity to be larger than extra-axonal diffusivity in spinal cord white matter, although not unambiguously. We compare our findings to predictions from effective medium theory.<br/>
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