Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation

Abstract Water diffusion anisotropy in the human brain is affected by disease, trauma, and development. Microscopic fractional anisotropy (μFA) is a diffusion MRI (dMRI) metric that can quantify water diffusion anisotropy independent of neuron fiber orientation dispersion. However, there are several different techniques to estimate μFA and few have demonstrated full brain imaging capabilities within clinically viable scan times and resolutions. Here, we present an optimized spherical tensor encoding (STE) technique to acquire μFA directly from the 2nd order cumulant expansion of the dMRI signal (i.e. diffusion kurtosis) which requires fewer powder-averaged signals than other STE fitting techniques and can be rapidly computed. We found that the optimal dMRI parameters for white matter μFA imaging were a maximum b-value of 2000 s/mm 2 and a ratio of isotropic to linear tensor encoded acquisitions of 1.7 for our system specifications. We then compared two implementations of the direct approach to the well-established gamma model in 4 healthy volunteers on a 3 Tesla system. One implementation of the direct cumulant approach used mean diffusivity (D) obtained from a 2nd order fit of the cumulant expansion, while the other used a linear estimation of D from the low b-values. Both implementations of the direct approach showed strong linear correlations with the gamma model (ρ=0.97 and ρ=0.90) but mean biases of −0.11 and −0.02 relative to the gamma model were also observed, respectively. All three μFA measurements showed good test-retest reliability (ρ≥0.79 and bias=0). To demonstrate the potential scan time advantage of the direct approach, 2 mm isotropic resolution μFA was demonstrated over a 10 cm slab using a subsampled data set with fewer powder-averaged signals that would correspond to a 3.3-minute scan. Accordingly, our results introduce an optimization procedure that has enabled clinically relevant, nearly full brain μFA in only several minutes. Highlights Demonstrated method to acquire optimal parameters for regression μFA imaging μFA measured using an optimized linear regression method at 3T First μFA comparison between direct regression approach and the gamma model Both approaches correlated strongly in white matter in healthy volunteers Nearly full brain μFA demonstrated in a 3.3-minute scan at 2 mm isotropic resolution

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