Summary The major goal of our parent grant (R01AT011419, “Lymphatics-Glymphatics in CNS Fluid Homeostasis”) supported by the NCCIH is focused on understanding glymphatic-lymphatic coupling in the healthy (rodent) brain. The glymphatic and lymphatic systems are pivotal for the control of central nervous system (CNS) fluid homeostasis and waste disposal. We are currently studying how physiological maneuvers such as changes in body posture and/or deep-inspiratory breathing affect the two systems and therefore be therapeutically beneficial for sustaining a healthy brain. However, an inherent problem for the timely development of complementary therapeutics is the technical challenge involved in tracking the functional interplay between the glymphatic and lymphatic systems, which have led to controversies regarding the directionality and driving forces of brain waste disposal. These controversies are thought to have arisen from heterogeneous experimental approaches, and most importantly from the lack of a robust computational framework for processing dynamic magnetic resonance imaging (MRI) optical imaging in vivo data. In our parent grant, we are addressing these challenges by establishing a data-driven, unified computational framework to describe glymphatic transport and brain clearance based on regularized optimal mass transport (rOMT) theory. We have developed a computational source code to process data derived from dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) acquired at the level of the head as well as the neck. However, it has become evident that several additional post-processing steps are needed for denoising the data, in particular, at the level of the neck where the DCE-MRI acquisitions are inherently noisy due to various physical degrading factors. We have already shared the raw rOMT code with the science community and also advanced an rOMT processing toolbox to incorporate the source term which will allow for tracking of waste clearance without any assumptions about mass preservation which may not hold in real-world data. However, although we shared the source code, only users with expensive MATLAB licenses and coding experience can run it, and more software engineering is required to develop a robust and useful framework software package for the user community. The goal of this administrative supplement is to: 1) implement and unify algorithms for temporal and spatial denoising of 4D DCE-MRI images to preserve the draining streams and anatomical structures in conjunction with rOMT flow tracking, and 2) refine our existing rOMT software framework and convert it into a user-friendly Python based package. Aim 1 is focused on developing the computational approach for denoising quantitative DCE-MRI data acquired at the neck and skull base, in particular. In Aim 2, we will convert the developed 4D denoising and rOMT fluid tracking pipeline into a cloud-ready format and integrate it into a plug-in-based graphical user interface...