# Academic Industrial Partnership on Advanced Perfusion MRI

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2024 · $397,136

## Abstract

PROJECT SUMMARY
Arterial spin labeled (ASL) perfusion MRI provides noninvasive quantification of tissue blood flow in physiological
units of ml/100g/min using magnetic labeling of blood water as an endogenous diffusible flow tracer and is one
of the few MRI parameters whose biological basis is known. ASL MRI has primarily been used in the brain to
measure cerebral blood flow (CBF), a key physiological parameter that serves a biomarker of cerebrovascular
integrity and regional brain function with a broad range of applications in basic and clinical neuroscience research
and in clinical care. ASL MRI was originally conceived by our laboratory at the University of Pennsylvania, and
we have been responsible for demonstrating many of its technical advances and applications in biomedical
research. Although ASL MRI has been translated to clinical use, commercial ASL MRI technologies have failed
to keep up with research progress.
This Academic Industrial Partnership in response to PAR-18-530 provides dedicated resources to develop,
disseminate, and maintain state-of-the-art ASL MRI acquisition and processing technologies for clinical research
on the Siemens MRI platform, which represents the most widely used MRI system in neuroscience. An Academic
Industrial Partnership is needed because market forces for commercial MRI technologies have been insufficient
to drive the development of state-of-the-art ASL MRI capabilities, yet close collaboration between academia and
industry are required to deliver a streamlined and capability to users. The proposed supplemental research in
response to NOT-AG-23-032
and
specifically focuses on optimizing ASL MRI for use in Alzheimer's disease
related dementia (ADRD) populations.
While a major innovation will be the delivery of an ASL MRI software package featuring state-of-the-art
approaches to maximize sensitivity, spatial and temporal resolution, and robustness to artifacts to meet evolving
research and clinical requirements for noninvasive quantification of regional cerebral blood flow, next-generation
approaches leveraging deep machine learning are also proposed to achieve higher spatial and temporal
resolution, faster online image reconstructions, and improved robustness to artifacts than are currently possible.
The proposed supplemental research leverages the interdisciplinary expertise of the investigative team to
provide a reliable, reproducible, flexible and user friendly technology for quantifying a key parameter of brain
health and function in ADRD research. The feasibility of the proposed work is supported by our preliminary data
and track record of ASL MRI technology development and dissemination.

## Key facts

- **NIH application ID:** 10939785
- **Project number:** 3R01EB031080-03S1
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** JOHN A DETRE
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $397,136
- **Award type:** 3
- **Project period:** 2022-05-01 → 2026-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10939785

## Citation

> US National Institutes of Health, RePORTER application 10939785, Academic Industrial Partnership on Advanced Perfusion MRI (3R01EB031080-03S1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10939785. Licensed CC0.

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