# Machine learning approach to non-invasive MRI-based blood oximetry

> **NIH NIH R21** · OHIO STATE UNIVERSITY · 2021 · $587,783

## Abstract

PROJECT SUMMARY
Measurement of blood oxygen (O2) saturation, the fraction of oxygen-saturated hemoglobin in blood, provides
information on whole-body and organ-specific O2 delivery and consumption and is used to guide therapy and
intervention. Blood sampling and analysis by invasive catheterization performed under X-ray fluoroscopic
guidance is the standard method used to measure O2 saturation in multiple anatomical locations in the cardiac
chambers and major blood vessels. Non-invasive measurement of O2 saturation using magnetic resonance (MR)
imaging was first proposed nearly 30 years ago; however, previous techniques have relied on fitting the Luz-
Meiboom model and other model variants using traditional linear and non-linear regression model approaches.
Although the model captures the basic underlying biophysical principles, it does not fully characterize the
complex relationship between blood O2 saturation and the MR signal. Despite being a non-invasive, non-
radiating alternative to invasive catheterization, the low accuracy of MR oximetry, due to inadequacy of the model
as well as estimation methods, have prevented the technique from gaining clinical acceptance. We propose to
overcome this critical limitation by meeting our overall objective; to deploy a model-free approach based on
machine learning (ML) to develop and implement an accurate, clinically feasible, MR oximetry technique. We
hypothesize that ML algorithms provide greater flexibility in parameter estimation than traditional methods, and
can be trained to learn and map the true in vivo relationship that describes the sensitivity of MR blood signal to
O2 saturation. We intend to achieve our objective through the following specific aims. In Aim 1, we will develop
a supervised ML algorithm for MR oximetry. Pre-training will occur with training data simulated using the L-M
model and then augmented with in vivo data via transfer learning. Simultaneously, in Aim 2, we will design and
implement a 3D MR oximetry method for volumetric data acquisition. A volumetric map will facilitate O2 saturation
measurement throughout the vascular system, and will support the combination with 4D flow to evaluate O2
delivery and consumption. In Aim 3, we will validate the proposed ML-based 3D MR oximetry technique in a
small cohort of patients referred for catheter-based O2 saturation measurement.
For the first time, our proposed work will apply machine learning to accurately characterize the in vivo sensitivity
of the transverse relaxation time (T2) weighted MR blood signal to O2 saturation, using a unique combination of
simulated and in vivo training data. ML-based MR oximetry will provide the accuracy of measurement required
for clinical use, and therefore will be able to replace or reduce the frequency and duration of an invasive,
radiation-based method with a safe, non-invasive alternative. ML-based MR oximetry as an imaging tool is
expected to significantly improve the diagnostic value of an MR e...

## Key facts

- **NIH application ID:** 10217710
- **Project number:** 1R21EB030294-01A1
- **Recipient organization:** OHIO STATE UNIVERSITY
- **Principal Investigator:** Juliet J. Varghese
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $587,783
- **Award type:** 1
- **Project period:** 2021-09-21 → 2025-09-20

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10217710

## Citation

> US National Institutes of Health, RePORTER application 10217710, Machine learning approach to non-invasive MRI-based blood oximetry (1R21EB030294-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10217710. Licensed CC0.

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