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Efficient estimation of propagator anisotropy and non‐Gaussianity in multishell diffusion MRI with micro‐structure adaptive convolution kernels and dual Fourier integral transforms
Author(s) -
París Guillem,
Pieciak Tomasz,
AjaFernández Santiago,
TristánVega Antonio
Publication year - 2023
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.29435
Subject(s) - propagator , isotropy , convolution (computer science) , fourier transform , diffusion mri , gaussian , computer science , fractional anisotropy , diffusion , algorithm , mathematics , artificial intelligence , statistical physics , mathematical analysis , physics , optics , artificial neural network , magnetic resonance imaging , medicine , quantum mechanics , mathematical physics , radiology , thermodynamics
Purpose We seek to reformulate the so‐called Propagator Anisotropy (PA) and Non‐Gaussianity (NG), originally conceived for the Mean Apparent Propagator diffusion MRI (MAP‐MRI), to the Micro‐Structure adaptive convolution kernels and dual Fourier Integral Transforms (MiSFIT). These measures describe relevant normalized features of the Ensemble Average Propagator (EAP). Theory and Methods First, the indices, which are defined as the EAP's dissimilarity from an isotropic (PA) or a Gaussian (NG) one, are analytically reformulated within the MiSFIT framework. Then a comparison between the resulting maps is drawn by means of a visual analysis, a quantitative assessment via numerical simulations, a test‐retest study across the MICRA dataset (6 subjects scanned five times) and, finally, a computational time evaluation. Results Findings illustrate the visual similarity between the indices computed with either technique. Evaluation against synthetic ground truth data, however, demonstrates MiSFIT's improved accuracy. In addition, the test–retest study reveals MiSFIT's higher degree of reliability in most of white matter regions. Finally, the computational time evaluation shows MiSFIT's time reduction up to two orders of magnitude. Conclusions Despite being a direct development on the MAP‐MRI representation, the PA and the NG can be reliably and efficiently computed within MiSFIT's framework. This, together with the previous findings in the original MiSFIT's article, could mean the difference that definitely qualifies diffusion MRI to be incorporated into regular clinical settings.