Improving PET scans to better detect coronary artery disease
Novel methodologies to improve coronary artery disease diagnostics with dynamic PET
['FUNDING_R01'] · UNIVERSITY OF CINCINNATI · NIH-11111387
Using new 4D PET image methods with motion correction and deep-learning noise reduction to get clearer blood-flow pictures for people with suspected coronary artery disease.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | UNIVERSITY OF CINCINNATI (nih funded) |
| Locations | 1 site (CINCINNATI, UNITED STATES) |
| Trial ID | NIH-11111387 on ClinicalTrials.gov |
What this research studies
This project aims to make heart PET scans show blood flow more clearly so doctors can spot blocked arteries sooner. The team will build new image-reconstruction methods that use the whole time-series of PET data (4D), correct for heart motion, and apply unsupervised deep learning to reduce noisy images. They'll test those methods first in the lab and in limited animal or patient data before broader use. If successful, the approach would be incorporated into dynamic 82Rb PET myocardial perfusion imaging used for coronary artery disease.
Who could benefit from this research
Good fit: People being evaluated for coronary artery disease who are referred for dynamic 82Rb PET myocardial perfusion imaging would be the ideal candidates.
Not a fit: People without suspected coronary artery disease or those who cannot undergo PET scans (for example, due to pregnancy or other contraindications) are unlikely to benefit directly from this work.
Why it matters
Potential benefit: Clearer, more accurate PET blood-flow images could help doctors diagnose coronary artery disease earlier and guide treatment decisions more confidently.
How similar studies have performed: Previous work shows 4D parametric PET can improve blood-flow measurement but has had only limited animal or patient validation, and combining motion correction with unsupervised deep-learning regularization is relatively novel.
Where this research is happening
CINCINNATI, UNITED STATES
- UNIVERSITY OF CINCINNATI — CINCINNATI, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: TANG, JING — UNIVERSITY OF CINCINNATI
- Study coordinator: TANG, JING
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 below.