Improving imaging techniques for human extremities using advanced computer algorithms
Official title: Unsupervised Deep Photon-Counting Computed Tomography Reconstruction for Human Extremity Imaging
Funded at University of Massachusetts Lowell · NIH-11090461 · Data updated 2026-11-12
This study is working on using smart computer technology to make CT scans of arms and legs clearer and safer, so patients can get better pictures of their tissues without as much radiation.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Massachusetts Lowell NIH-funded |
| Lab location | 1 site (Lowell, United States) |
| Project start | July 1, 2023 |
| Project end | April 30, 2027 |
| Funding year | 2025 |
| Award (this year) | $561,703 |
| NIH project number | 5R01EB034737-03 |
| Project ID | NIH-11090461 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals requiring imaging of their extremities, such as those with injuries or conditions affecting bones and soft tissues.
✕Less likely to help
Patients who do not require imaging of their extremities or those with conditions that do not involve the use of CT imaging may not benefit from this research.
What this research studies
This research focuses on enhancing computed tomography (CT) imaging for human extremities by utilizing advanced deep learning algorithms. The approach aims to improve image quality while reducing radiation exposure, making it safer for patients. By leveraging big data, the study seeks to train a deep learning network that can reconstruct high-quality images from x-ray photon-counting CT scans. This could lead to better tissue characterization and more accurate diagnoses.
Why it matters
Potential benefit: If successful, this research could provide patients with safer and more accurate imaging techniques for diagnosing conditions affecting their extremities.
How similar studies have gone: Other research has shown promise in using deep learning for image reconstruction, indicating that this approach could lead to significant advancements in imaging technology.
Where this research is happening
-
University of Massachusetts Lowell
Lowell, United States Active
Researchers
- Principal investigator: Yu, Hengyong — University of Massachusetts Lowell
- Study coordinator: Yu, Hengyong
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Unsupervised photon-counting CT reconstruction for extremity imaging
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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.