High-resolution heart MRI and AI to understand poor heart relaxation
Official title: Integrated super-resolution CMR-deep learning to deconvolute passive and active causes of impaired relaxation
Funded at Texas Engineering Experiment Station · NIH-11298963 · Data updated 2026-11-12
This project uses high-resolution cardiac MRI combined with artificial intelligence to tell apart stiffness and active relaxation problems in people whose hearts don't relax well, such as those with HFpEF or hypertrophic cardiomyopathy.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Texas Engineering Experiment Station NIH-funded |
| Lab location | 1 site (College Station, United States) |
| Project start | March 6, 2025 |
| Project end | December 31, 2029 |
| Funding year | 2026 |
| Award (this year) | $599,716 |
| NIH project number | 5R01HL168368-02 |
| Project ID | NIH-11298963 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults with diagnoses or symptoms of left ventricular diastolic dysfunction such as heart failure with preserved ejection fraction, hypertrophic cardiomyopathy, or diabetic cardiomyopathy would be the ideal candidates.
✕Less likely to help
People whose problems are mainly systolic heart failure, isolated valve disease, or non-cardiac causes of symptoms may not benefit from this specific imaging approach.
What this research studies
This work will combine super-resolution cardiac MRI (to get very detailed images of heart structure and motion) with deep learning models that analyze how the heart muscle relaxes. The team will use imaging data and computational models to separate passive stiffness (from fibrosis or thickening) from active relaxation problems at the muscle fiber level. They will validate the approach with complementary lab and imaging data so the measurements reflect real biological causes. If successful, the method would allow doctors to see which mechanism is driving a patient’s impaired relaxation without invasive tests.
Why it matters
Potential benefit: If successful, this could help doctors pick treatments that target the true cause of a patient’s poor heart relaxation, improving care and avoiding ineffective therapies.
How similar studies have gone: Researchers have used MRI and AI to improve heart imaging before, but using these tools to separate passive versus active causes of impaired relaxation in living patients is largely new and not yet proven.
Where this research is happening
-
Texas Engineering Experiment Station
College Station, United States Active
Researchers
- Principal investigator: Avazmohammadi, Reza — Texas Engineering Experiment Station
- Study coordinator: Avazmohammadi, Reza
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About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.