Using advanced technology to improve medical imaging performance
Official title: Deep learning technologies for estimating the optimal task performance of medical imaging systems
Funded at University of Illinois at Urbana-Champaign · NIH-10911790 · Data updated 2026-11-12
This study is working on improving medical imaging technology, like X-rays and MRIs, by creating virtual tests that help measure how clear and useful the images are for diagnosing health issues, with the goal of making these tools better for doctors and patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Urbana-Champaign NIH-funded |
| Lab location | 1 site (Champaign, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2027 |
| Funding year | 2024 |
| Award (this year) | $447,677 |
| NIH project number | 5R01EB034249-02 |
| Project ID | NIH-10911790 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients undergoing diagnostic imaging procedures who may benefit from enhanced imaging technologies.
✕Less likely to help
Patients who do not require medical imaging or those with conditions that do not involve imaging diagnostics may not receive benefit from this research.
What this research studies
This research focuses on enhancing medical imaging systems by developing virtual imaging trials that simulate and analyze imaging experiments. It aims to create new computational methods to objectively measure image quality, which can help identify how well images can be used for different diagnostic tasks. By assessing the best possible performance of imaging systems, the research seeks to improve the effectiveness of medical imaging technologies, ultimately benefiting patient diagnosis and treatment. The approach is designed to be open source and applicable across various imaging modalities.
Why it matters
Potential benefit: If successful, this research could lead to significantly improved diagnostic accuracy and efficiency in medical imaging, benefiting patient care.
How similar studies have gone: Other research has shown success in using computational methods to enhance medical imaging, indicating that this approach has potential for significant advancements.
Where this research is happening
-
University of Illinois at Urbana-Champaign
Champaign, United States Active
Researchers
- Principal investigator: Anastasio, Mark a — University of Illinois at Urbana-Champaign
- Study coordinator: Anastasio, Mark a
Related research
Other NIH-funded projects on the same topics.
-
Integrative histo-genomic analysis to guide cancer risk and targeted therapies with interpretable AI
Researchers are developing an advanced AI-powered approach to map genetic and cellular patterns across breast and cervical cancers to help tailor treatments.
-
Predicting how antimalarial drug resistance spreads with AI
A powerful AI model will be built to forecast how resistance to antimalarial drugs might spread in Africa and beyond, helping clinicians and public health teams stay ahead of outbreaks.
-
Self-supervised deep learning for biological sequences
A deep learning project uses self-supervised methods to better understand biological sequences and related data, with the goal of advancing biology and medicine.
-
Mobile phone imaging and AI to screen oral cancers in low-resource settings
A low-cost mobile phone device with artificial intelligence is being developed to help frontline health workers screen for oral cancer early in low-resource areas.
-
Linking Genetic Variants to Real-Life Health Effects with Smart AI
A new AI-powered approach will predict how genetic differences affect health across multiple levels to help doctors understand diseases and tailor care.
-
Integrative AI tools to map protein sequences to structures and functions
A scientist team will develop advanced AI methods to better connect protein sequences, their 3D shapes, and how they work, using large biological data from sequencing and structures.
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.