Regularized Optimal Mass Transport with Nonlinear Diffusion

TL;DR

This paper introduces a novel approach combining nonlinear diffusion with regularized optimal mass transport to improve fluid flow analysis in the brain, especially in glymphatic transport imaging.

Contribution

It presents a new framework integrating anisotropic diffusion into rOMT, enhancing the analysis of brain fluid dynamics from MRI data.

Findings

Captures larger advection-dominant volume in glymphatic transport.

Provides deeper insights into brain fluid flow analysis.

Enhances image processing with edge-aware diffusion.

Abstract

In this paper, we combine nonlinear diffusion with the regularized optimal mass transport (rOMT) model. As we will demonstrate, this new approach provides further insights into certain applications of fluid flow analysis in the brain. From the point of view of image processing, the anisotropic diffusion method, based on Perona-Malik, explicitly considers edge information. Applied to rOMT analysis of glymphatic transport based on DCE-MRI data, this new framework appears to capture a larger advection-dominant volume.