Improving chemical imaging for better cancer detection during surgery
Official title: Super-resolution chemical imaging via a diffusion-based deep generative model
Funded at University of Washington · NIH-11139592 · Data updated 2026-11-12
This study is working on a new computer program that helps doctors get clearer and faster images during cancer surgeries, making it easier for them to spot and remove any cancerous tissue right away.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Washington NIH-funded |
| Lab location | 1 site (Seattle, United States) |
| Project start | August 1, 2024 |
| Project end | May 31, 2026 |
| Funding year | 2025 |
| Award (this year) | $160,712 |
| NIH project number | 5R21EB036205-02 |
| Project ID | NIH-11139592 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients undergoing surgical procedures for cancer treatment who may benefit from enhanced imaging techniques.
✕Less likely to help
Patients who are not undergoing surgery or those with non-cancerous conditions may not receive any benefit from this research.
What this research studies
This research aims to develop a new computational model that enhances the quality and speed of chemical imaging techniques used in cancer detection during surgeries. By utilizing advanced artificial intelligence methods, the project seeks to transform low-resolution images into high-resolution ones, allowing for more accurate and timely diagnoses. This could significantly improve the ability of surgeons to identify and remove cancerous tissues in real-time, reducing the chances of leaving behind cancerous cells. The approach focuses on overcoming current limitations in imaging resolution and throughput, which are critical for effective intraoperative decision-making.
Why it matters
Potential benefit: If successful, this research could lead to more accurate cancer surgeries and better patient outcomes by enabling real-time feedback during operations.
How similar studies have gone: While super-resolution imaging is a novel approach in this specific context, advancements in AI-driven imaging techniques have shown promise in other areas of medical imaging.
Where this research is happening
-
University of Washington
Seattle, United States Active
Researchers
- Principal investigator: Fu, Dan — University of Washington
- Study coordinator: Fu, Dan
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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.