Improving CT imaging using advanced deep learning techniques
Official title: DL-based CT image formation with characterization and control of resolution and noise
Funded at Johns Hopkins University · NIH-10911914 · Data updated 2026-11-12
This study is testing a new way to make CT scans clearer and easier for doctors to read, which could help them make better diagnoses and improve care for patients.
Quick facts
| Grant type | R21 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Johns Hopkins University NIH-funded |
| Lab location | 1 site (Baltimore, United States) |
| Project start | September 1, 2023 |
| Project end | July 31, 2026 |
| Funding year | 2024 |
| Award (this year) | $200,788 |
| NIH project number | 5R21EB033426-02 |
| Project ID | NIH-10911914 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals undergoing CT imaging for various medical evaluations, particularly those requiring precise imaging for diagnosis.
✕Less likely to help
Patients who do not require CT imaging or those with conditions that do not involve imaging diagnostics may not benefit from this research.
What this research studies
This research focuses on enhancing the quality of CT images through a novel deep learning framework called GradDNN. By characterizing and controlling the resolution and noise in CT images during the image formation process, the study aims to produce clearer and more interpretable images for radiologists. The approach involves training a deep neural network that not only generates images but also ensures that these images meet specific quality standards. This could lead to more accurate diagnoses and better patient outcomes.
Why it matters
Potential benefit: If successful, this research could significantly improve the clarity and diagnostic utility of CT images, leading to better patient care.
How similar studies have gone: Other research in the field of deep learning for medical imaging has shown promising results, indicating that this approach could lead to significant advancements.
Where this research is happening
-
Johns Hopkins University
Baltimore, United States Active
Researchers
- Principal investigator: Xu, Jingyan — Johns Hopkins University
- Study coordinator: Xu, Jingyan
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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.