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Frequency-dependent diffusion MRI (dMRI) using oscillating gradient encodingand diffusion kurtosis imaging (DKI) techniques have been shown to provideadditional insight into tissue microstructure compared to conventional dMRI.However, a technical challenge when combining these techniques is that thegeneration of the large b-values required for DKI is difficult when usingoscillating gradient diffusion encoding. While efficient encoding schemes canenable larger b-values by maximizing multiple gradient channels simultaneously,they do not have sufficient directions to enable fitting of the full kurtosistensor. Accordingly, we investigate a DKI fitting algorithm that combinesaxisymmetric DKI fitting, a prior that enforces the same axis of symmetry forall oscillating gradient frequencies, and spatial regularization, whichtogether enable robust DKI fitting for a 10-direction scheme that offers doublethe b-value compared to traditional direction schemes. Using data from mice(oscillating frequencies of 0, 60, and 120 Hz) and humans (0 Hz only), we firstshow that axisymmetric modelling is advantageous over full kurtosis tensorfitting in terms of preserving contrast and reducing noise in DKI maps, andimproved DKI map quality when using an efficient encoding scheme with averagingas compared to a traditional scheme with more encoding directions. We alsodemonstrate how spatial regularization during fitting preserves spatialfeatures better than using Gaussian filtering prior to fitting, which is anoft-reported preprocessing step for DKI, and that enforcing consistent axes ofsymmetries across frequencies improves fitting quality. Thus, the use of anefficient 10-direction scheme combined with the proposed DKI fitting algorithmprovides robust maps of frequency-dependent directional kurtosis parametersthat can be used to explore novel biomarkers for various pathologies.

Robust frequency-dependent diffusion kurtosis computation using an efficient direction scheme, axisymmetric modelling, and spatial regularization