GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport

TL;DR

GlymphVIS introduces a novel visualization method using regularized optimal transport to analyze glymphatic transport pathways in MRI data, improving accuracy and aligning with expert findings.

Contribution

The paper presents GlymphVIS, a new framework employing regularized optimal transport for dynamic visualization of glymphatic flow in brain MRI, enhancing accuracy and noise handling.

Findings

Reduces registration error by up to 5 times

Accurately captures time-varying glymphatic flow

Aligns well with expert observations

Abstract

The glymphatic system (GS) is a transit passage that facilitates brain metabolic waste removal and its dysfunction has been associated with neurodegenerative diseases such as Alzheimer's disease. The GS has been studied by acquiring temporal contrast enhanced magnetic resonance imaging (MRI) sequences of a rodent brain, and tracking the cerebrospinal fluid injected contrast agent as it flows through the GS. We present here a novel visualization framework, GlymphVIS, which uses regularized optimal transport (OT) to study the flow behavior between time points at which the images are taken. Using this regularized OT approach, we can incorporate diffusion, handle noise, and accurately capture and visualize the time varying dynamics in GS transport. Moreover, we are able to reduce the registration mean-squared and infinity-norm error across time points by up to a factor of 5 as compared to the current state-of-the-art method. Our visualization pipeline yields flow patterns that align well with experts' current findings of the glymphatic system.