Analyzing signal attenuation in PFG anomalous diffusion via a non-Gaussian phase distribution approximation approach by fractional derivatives
Guoxing Lin

TL;DR
This paper introduces a novel non-Gaussian phase distribution approximation based on fractional derivatives to accurately model signal attenuation in PFG anomalous diffusion, including finite pulse width effects, advancing the interpretation of complex diffusion in NMR and MRI.
Contribution
The paper proposes the MLP phase distribution approximation, providing an exact signal attenuation expression for PFG anomalous diffusion with fractional derivatives, including finite gradient pulse width effects.
Findings
MLP distribution leads to Mittag-Leffler function-based attenuation
The model agrees with existing literature results
Provides a new formalism for complex PFG fractional diffusion interpretation
Abstract
Anomalous diffusion exists widely in polymer and biological systems. Pulsed field gradient (PFG) techniques have been increasingly used to study anomalous diffusion in NMR and MRI. However, the interpretation of PFG anomalous diffusion is complicated. Moreover, there is not an exact signal attenuation expression based on fractional derivatives for PFG anomalous diffusion, which includes the finite gradient pulse width effect. In this paper, a new method, a Mainardi-Luchko-Pagnini (MLP) phase distribution approximation, is proposed to describe PFG fractional diffusion. MLP phase distribution is a non-Gaussian phase distribution. From the fractional diffusion equation based on fractional derivatives in both real space and phase space, the obtained probability distribution function is a MLP distribution. The MLP distribution leads to a Mittag-Leffler function based PFG signal attenuation…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
