Robust, fast and accurate mapping of diffusional mean kurtosis

TL;DR

This paper introduces a novel sub-diffusion framework for rapid, robust, and accurate estimation of mean kurtosis in diffusion MRI, overcoming previous limitations and enabling clinical application with minimal scan time.

Contribution

It presents a new sub-diffusion model for kurtosis estimation, a fast fitting procedure using two diffusion times, and demonstrates effectiveness on simulated and real brain data.

Findings

Achieves high tissue contrast in minutes

Overcomes b-value limitations of traditional DKI

Provides robust and accurate kurtosis maps

Abstract

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.