Improving PET imaging accuracy using AI technology
Official title: Multi-Center Academic-Industrial Partnership for Personalized Al-Enabled High Count PET
Funded at Yale University · NIH-11067735 · Data updated 2026-11-12
This study is working on improving PET scans by using smart computer techniques to make the images clearer, which could help doctors make more accurate diagnoses and reduce the need for extra tests or procedures for patients.
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 start | May 5, 2023 |
| Project end | April 30, 2028 |
| Funding year | 2025 |
| Award (this year) | $629,316 |
| NIH project number | 5R01CA275188-03 |
| Project ID | NIH-11067735 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients undergoing PET imaging for diagnostic purposes, particularly those with conditions that require precise imaging.
✕Less likely to help
Patients who are not undergoing PET imaging or those with conditions that do not require imaging diagnostics may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of PET imaging by utilizing advanced deep learning techniques to reduce image noise. High image noise can lead to incorrect diagnoses and unnecessary follow-up procedures, so the project aims to develop methods that convert standard clinical PET images into higher-quality images. By collaborating with leading academic centers and a prominent imaging company, the research seeks to create robust algorithms that can be applied across various clinical settings. Patients may benefit from more accurate diagnoses and reduced need for invasive procedures.
Why it matters
Potential benefit: If successful, this research could lead to more accurate PET imaging, improving diagnosis and reducing unnecessary medical interventions for patients.
How similar studies have gone: Previous research has shown promise in using deep learning for noise reduction in imaging, indicating potential success for this novel approach.
Where this research is happening
-
Yale University
New Haven, United States Active
Researchers
- Principal investigator: Liu, Chi — Yale University
- Study coordinator: Liu, Chi
Related research
Other NIH-funded projects on the same topics.
-
Aging Well with HIV: Alcohol, Risk Reduction, and Education Center (AWAR3E)
A center at Yale is exploring how aging with HIV interacts with alcohol use to create inflammation and frailty, with the goal of finding biomarkers and potential treatments.
-
Community Health Worker–Led Hypertension Prevention and Control in Nepal
A community health worker–driven approach in Nepal aims to prevent and control high blood pressure among adults by bringing education and care directly to communities and homes.
-
Community health worker-led hypertension prevention and control in Nepal
A community health worker–led program in Nepal aims to help adults prevent and better control high blood pressure through home visits, education, and ongoing support.
-
Adapting a mobile health tool to boost HIV outcomes for older adults with HIV in Ukraine
A mobile health platform called PositiveLinks would be adapted for older adults with HIV in Ukraine to help them stay engaged in care and improve treatment success.
-
Behavioral and neural links between spoken language sounds and written words in young children
This grant studies how young children learn from listening and reading to improve literacy, by looking at how sound patterns transfer to written language.
-
Understanding how autoimmunity causes myasthenia gravis
This project looks at why the immune system attacks the nerve-muscle connection in myasthenia gravis and how different patient groups are affected.
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.