# Robust workflow software for MRI tracking of glymphatic-lymphatic coupling

> **NIH NIH R01** · YALE UNIVERSITY · 2022 · $242,224

## Abstract

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...

## Key facts

- **NIH application ID:** 10609195
- **Project number:** 3R01AT011419-02S1
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Helene D Benveniste
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $242,224
- **Award type:** 3
- **Project period:** 2021-04-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10609195

## Citation

> US National Institutes of Health, RePORTER application 10609195, Robust workflow software for MRI tracking of glymphatic-lymphatic coupling (3R01AT011419-02S1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10609195. Licensed CC0.

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