Improving PET imaging accuracy using AI technology
Multi-Center Academic-Industrial Partnership for Personalized Al-Enabled High Count PET
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 ID | NIH-11067735 on NIH RePORTER |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research are patients undergoing PET imaging for diagnostic purposes, particularly those with conditions that require precise imaging.
Not a fit: Patients who are not undergoing PET imaging or those with conditions that do not require imaging diagnostics may not benefit from this research.
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 performed: 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
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.