  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.

Computer Vision and Pattern Recognition