GlymphVIS: Visualizing Glymphatic Transport Pathways Using Regularized Optimal Transport

• Published in: Lecture notes in computer science Vol. 11070; p. 844
• Main Authors: Elkin, Rena, Nadeem, Saad, Haber, Eldad, Steklova, Klara, Lee, Hedok, Benveniste, Helene, Tannenbaum, Allen
• Format: Journal Article Book Chapter
• Language: English
• Published: Germany 01.01.2018
• Subjects: Algorithms; Alzheimer Disease - diagnostic imaging; Brain - diagnostic imaging; Contrast Media; Glymphatic System - diagnostic imaging; Humans; Magnetic Resonance Imaging; Reproducibility of Results; Sensitivity and Specificity
• ISSN: 1611-3349; 0302-9743
• DOI: 10.1007/978-3-030-00928-1_95

The glymphatic system (GS) is a transit passage that facil-itates brain metabolic waste removal and its dysfunction has been asso-ciated 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 app-roach, 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.