Abstract
Microscopic diffusion anisotropy imaging using diffusion-weighted MRI and multidimensional diffusion encoding is a promising method for quantifying clinically and scientifically relevant microstructural properties of neural tissue. Several methods for estimating microscopic fractional anisotropy (µFA), a normalized measure of microscopic diffusion anisotropy, have been introduced but the differences between the methods have received little attention thus far. In this study, the accuracy and precision of µFA estimation using q-space trajectory encoding and different signal models were assessed using imaging experiments and simulations. Three healthy volunteers and a microfibre phantom were imaged with five non-zero b-values and gradient waveforms encoding linear and spherical b-tensors. Since the ground-truth µFA was unknown in the imaging experiments, Monte Carlo random walk simulations were performed using axon-mimicking fibres for which the ground truth was known. Furthermore, parameter bias due to time-dependent diffusion was quantified by repeating the simulations with tuned waveforms, which have similar power spectra, and with triple diffusion encoding, which, unlike q-space trajectory encoding, is not based on the assumption of time-independent diffusion. The truncated cumulant expansion of the powder-averaged signal, gamma-distributed diffusivities assumption, and q-space trajectory imaging, a generalization of the truncated cumulant expansion to individual signals, were used to estimate µFA. The gamma-distributed diffusivities assumption consistently resulted in greater µFA values than the second order cumulant expansion, 0.1 greater when averaged over the whole brain. In the simulations, the generalized cumulant expansion provided the most accurate estimates. Importantly, although time-dependent diffusion caused significant overestimation of µFA using all the studied methods, the simulations suggest that the resulting bias in µFA is less than 0.1 in human white matter.
Highlights
A visual inspection reveals that the higher-order model and gamma model result in greater microscopic fractional anisotropy (μFA) values than the cumulant model and q-space trajectory imaging (QTI)
The purpose of this study was to assess the accuracy and precision of μFA estimates calculated using different signal models. μFA was chosen as the metric of interest for its connection to conventional fractional anisotropy (FA) that is well known in the neuroscience community
The higher-order model was included in the study for it being a simple extension to the cumulant model that has been shown to improve the accuracy of μFA estimation in animal studies using double diffusion encoding (DDE) (Ianuş et al, 2018)
Summary
Diffusion-weighted magnetic resonance imaging (dMRI) has become firmly established as the MRI technique of choice for quantify-NeuroImage 242 (2021) 118445 ing neural tissue’s microstructural properties in vivo (Johansen-Berg and Behrens, 2013). Because orientation dispersion of anisotropic neurites can result in an isotropic diffusion tensor, several methods have been developed to obtain a more accurate estimate of the fibre orientation distribution, e.g., (Tournier et al, 2007; Tuch, 2004; Wedeen et al, 2008) Another major limitation of DTI is that it does not provide a good fit to data in experiments with moderate to high diffusion-weighting (roughly b < ms∕μm in the brain) when the signal as a function of b-value clearly deviates from a monoexponential decay, revealing that the voxel-level diffusion propagator is not Gaussian, especially in white matter (Jensen et al, 2005). Comprehensive overviews of the state of the field are provided by Jelescu and Budde (2017) and Novikov et al (2019), for example
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