GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport
This work addresses the need for better visualization tools in neuroscience to study waste removal in the brain, which is linked to neurodegenerative diseases like Alzheimer's, but it is incremental as it builds on existing optimal transport methods.
The authors tackled the problem of visualizing glymphatic transport pathways in the brain by developing GlymphVIS, a framework using regularized optimal transport to analyze MRI data, which reduced registration errors by up to a factor of 5 compared to state-of-the-art methods.
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.