DRAC: A Deep Learning Method to Reconstruct Safer Multi-Phase Cardiac CT Angiography
Official title: Deep leaRning-bAsed reConstruction (DRAC) for multi-phase cardiac CT angiography
Funded at University of Texas Arlington · NIH-11292231 · Data updated 2026-11-12
A new AI-based method aims to dramatically cut radiation while keeping or improving the quality of multi-phase heart CT scans for people at risk of coronary artery disease.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Texas Arlington NIH-funded |
| Lab location | 1 site (Arlington, United States) |
| Project start | July 25, 2026 |
| Project end | June 30, 2029 |
| Funding year | 2026 |
| Award (this year) | $579,745 |
| NIH project number | 1R15HL184249-01 |
| Project ID | NIH-11292231 on NIH RePORTER |
Who could benefit from this research
✓Could help
Adults undergoing cardiac CT angiography for assessment of suspected or known coronary artery disease.
✕Less likely to help
Individuals unlikely to require multi-phase CTA or those with contraindications to CT imaging may not benefit.
What this research studies
This project develops a deep learning reconstruction approach called DRAC that combines model-based techniques with learned image features to lower radiation dose in multi-phase cardiac CT angiography. By leveraging a large database of MP-CTA images, the method aims to maintain or improve diagnostic quality while reducing exposure, potentially making high-quality heart imaging safer for patients. The work focuses on translating 4D data (3D over time) into clearer images with fewer artifacts, which could help clinicians better assess coronary disease without added risk. If successful, patients undergoing CTA could receive safer scans with preserved or enhanced information about heart function and anatomy.
Why it matters
Potential benefit: If successful, patients could benefit from lower radiation exposure during cardiac CT scans without sacrificing diagnostic accuracy.
How similar studies have gone: Previous imaging reconstruction efforts show promise in reducing dose, but this approach uniquely combines MBIR with deep learning using a large MP-CTA database.
Where this research is happening
-
University of Texas Arlington
Arlington, United States Active
Researchers
- Principal investigator: Jin, Mingwu — University of Texas Arlington
- Study coordinator: Jin, Mingwu
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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.