Visualizing fluid flows via regularized optimal mass transport with applications to neuroscience

TL;DR

This paper introduces a regularized optimal mass transport model with diffusion for visualizing fluid flows, specifically applied to the glymphatic system, with improved computational efficiency demonstrated on synthetic and real data.

Contribution

It extends the rOMT framework with a modified numerical method, enabling faster visualization of fluid flows in neuroscience applications.

Findings

Reduced computational runtime for rOMT simulations

Effective visualization of glymphatic system fluid flows

Successful application to synthetic and real data

Abstract

Regularized optimal mass transport (rOMT) problem adds a diffusion term to the continuity equation in the original dynamic formulation of the optimal mass transport (OMT) problem proposed by Benamou and Brenier. We show that the rOMT model serves as a powerful tool in computational fluid dynamics (CFD) for visualizing fluid flows in the glymphatic system. In the present work, we describe how to modify the previous numerical method for efficient implementation, resulting in a significant reduction in computational runtime. Numerical results applied to synthetic and real-data are provided.