AI to make PET scans clearer for each patient
Multi-Center Academic-Industrial Partnership for Personalized Al-Enabled High Count PET
This project uses artificial intelligence to turn routine PET scans into higher-count, less noisy images so people getting PET scans can have clearer results.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Yale University NIH-funded |
| Lab location | 1 site (New Haven, United States) |
| Project ID | NIH-11320723 on NIH RePORTER |
What this research studies
If I get a PET scan at one of the participating centers, researchers will use deep-learning software to reduce image noise and produce images that look like higher-count scans. The team collected many real PET images from different hospitals and scanners to train the AI so it works across machines and settings. Partner hospitals and an imaging company will test the software on clinical scans and work to integrate it into routine reading. The goal is to make images clearer without changing how my scan is done.
Who could benefit from this research
Good fit: People scheduled for routine PET imaging (for example for cancer diagnosis, staging, or monitoring) at participating centers are the most likely candidates for this work.
Not a fit: Patients who do not receive PET scans, whose scans are already very high-count/low-noise, or who are scanned at non-participating sites may not experience benefit.
Why it matters
Potential benefit: Clearer, higher-quality PET images could lower false positives, reduce unnecessary follow-up tests or procedures, and increase diagnostic confidence.
How similar studies have performed: Deep-learning noise reduction for low-count PET has shown promise in prior work, but converting routine clinical PET to consistently higher-count images across many scanners is a newer and less-tested approach.
Where this research is happening
New Haven, United States
- Yale University — New Haven, United States (Active)
Researchers
- Principal investigator: Liu, Chi — Yale University
- Study coordinator: Liu, Chi
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.