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Diffusional kurtosis imaging (DKI) is a methodology for measuring the extentof non-Gaussian diffusion in biological tissue, which has shown great promisein clinical diagnosis, treatment planning and monitoring of many neurologicaldiseases and disorders. However, robust, fast and accurate estimation ofkurtosis from clinically feasible data acquisitions remains a challenge. Inthis study, we first outline a new accurate approach of estimating meankurtosis via the sub-diffusion mathematical framework. Crucially, thisextension of the conventional DKI overcomes the limitation on the maximumb-value of the latter. Kurtosis and diffusivity can now be simply computed asfunctions of the sub-diffusion model parameters. Second, we propose a new fastand robust fitting procedure to estimate the sub-diffusion model parametersusing two diffusion times without increasing acquisition time as for theconventional DKI. Third, our sub-diffusion based kurtosis mapping method isevaluated using both simulations and the Connectome 1.0 human brain data.Exquisite tissue contrast is achieved even when the diffusion encoded data iscollected in only minutes. In summary, our findings suggest robust, fast andaccurate estimation of mean kurtosis can be realised within a clinicallyfeasible diffusion weighted magnetic resonance imaging data acquisition time.

Robust, fast and accurate mapping of diffusional mean kurtosis