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