# Measuring Brain Health Using Low-Field Portable MRI

> **NIH NIH R21** · YALE UNIVERSITY · 2024 · $476,993

## Abstract

PROJECT SUMMARY/ABSTRACT
White matter hyperintensity (WMH) seen on MRI of the brain is an important biomarker of elevated risk for stroke
and Alzheimer's disease and related dementias. There are qualitative scales to measure WMH severity as well
as automated techniques that quantify WMH volumetrically. However, these techniques – manual or automated
– were developed for conventional high-field MRI and are not optimized for the unique imaging characteristics
of low-field portable MRI (pMRI). The pMRI device costs a fraction of a conventional high-field MRI, is FDA
approved, does not require magnetic shielding, can be rolled from room to room, and plugged into a standard
wall socket. Taking into account the unique imaging attributes of pMRI, we will create both a qualitative low-field
WMH scale that can be used widely and a machine learning enabled quantitative measurement of WMH for
more sophisticated applications. To ensure the reliability of these WMH measurement systems, we will enroll
100 participants who will receive both a pMRI and high-field 3T MRI at a single study visit for the purpose of
comparing WMH measurements against a gold standard (3T MRI). Using the Delphi method, an expert panel of
pMRI researchers will develop the low-field WMH grading scale, iteratively refine it, and validate it within this
cohort. Parallel to this, advanced machine learning methodologies will be utilized in this cohort, allowing for
precise quantification of WMH volume on pMRI. These advances are possible because our multidisciplinary
team has expertise that spans translational vascular research to MRI physics and computational medical
imaging. However, our vision transcends merely introducing a novel imaging measurement method; we aspire
to make brain health assessments more universally accessible and economically feasible compared to the
current hospital-based high-field MRI. Upon validation of these WMH quantification methodologies, we will
immediately make them available in our popular software package, FreeSurfer (over 60,000 worldwide licenses),
and implement them in our ongoing pMRI-based assessments of brain health in real-world settings including a
safety net emergency department and at a health center providing primary care to underserved and understudied
communities. Combining pMRI innovation with rigorous measurement techniques, we aspire to widen the reach
of brain health evaluations, encompassing a more diverse beneficiary group. By permitting early detection of
WMH in a range of healthcare or community settings, our project holds the potential to refine intervention
strategies, particularly for debilitating conditions like stroke and Alzheimer's disease and related dementias.

## Key facts

- **NIH application ID:** 10950708
- **Project number:** 1R21NS138995-01
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Adam H. de Havenon
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $476,993
- **Award type:** 1
- **Project period:** 2024-08-01 → 2026-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10950708

## Citation

> US National Institutes of Health, RePORTER application 10950708, Measuring Brain Health Using Low-Field Portable MRI (1R21NS138995-01). Retrieved via AI Analytics 2026-05-27 from https://api.ai-analytics.org/grant/nih/10950708. Licensed CC0.

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