Efficient estimation of propagator anisotropy and non‐Gaussianity in multishell diffusion MRI with micro‐structure adaptive convolution kernels and dual Fourier integral transforms

Abstract

Purpose:We seek to reformulate the so-called Propagator Anisotropy (PA) andNon-Gaussianity (NG), originally conceived for the Mean Apparent Propagatordiffusion MRI (MAP-MRI), to the Micro-Structure adaptive convolution ker-nels and dual Fourier Integral Transforms (MiSFIT). These measures describerelevant normalized features of the Ensemble Average Propagator (EAP).Theory and Methods:First, the indices, which are defined as the EAP’sdissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analyticallyreformulated within the MiSFIT framework. Then a comparison between theresulting maps is drawn by means of a visual analysis, a quantitative assess-ment via numerical simulations, a test-retest study across the MICRA dataset (6subjects scanned five times) and, finally, a computational time evaluation.Results:Findings illustrate the visual similarity between the indices computedwith either technique. Evaluation against synthetic ground truth data, however,demonstrates MiSFIT’s improved accuracy. In addition, the test–retest studyreveals MiSFIT’s higher degree of reliability in most of white matter regions.Finally, the computational time evaluation shows MiSFIT’s time reduction upto two orders of magnitude.Conclusions:Despite being a direct development on the MAP-MRI represen-tation, the PA and the NG can be reliably and efficiently computed withinMiSFIT’s framework. This, together with the previous findings in the originalMiSFIT’s article, could mean the difference that definitely qualifies diffusionMRI to be incorporated into regular clinical settings.