Improving CT imaging using advanced deep learning techniques
DL-based CT image formation with characterization and control of resolution and noise
['FUNDING_R21'] · JOHNS HOPKINS UNIVERSITY · NIH-10911914
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
| Phase | ['FUNDING_R21'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | JOHNS HOPKINS UNIVERSITY (nih funded) |
| Locations | 1 site (BALTIMORE, UNITED STATES) |
| Trial ID | NIH-10911914 on ClinicalTrials.gov |
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.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals undergoing CT imaging for various medical evaluations, particularly those requiring precise imaging for diagnosis.
Not a fit: Patients who do not require CT imaging or those with conditions that do not involve imaging diagnostics may not benefit from this research.
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 performed: 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
BALTIMORE, UNITED STATES
- JOHNS HOPKINS UNIVERSITY — BALTIMORE, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: XU, JINGYAN — JOHNS HOPKINS UNIVERSITY
- Study coordinator: XU, JINGYAN
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.