Using AI to improve MRI image quality and correct errors
Official title: AI-Powered MRI Quality Control and Artifact Correction for Multi-Site Studies
Funded at Univ of North Carolina Chapel Hill · NIH-11042259 · Data updated 2026-11-12
This study is working on using artificial intelligence to improve MRI images of the brain, making sure they are clear and accurate, so researchers can better understand brain health and function.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Univ of North Carolina Chapel Hill NIH-funded |
| Lab location | 1 site (Chapel Hill, United States) |
| Project start | March 1, 2024 |
| Project end | February 29, 2028 |
| Funding year | 2025 |
| Award (this year) | $488,010 |
| NIH project number | 5R01EB035160-02 |
| Project ID | NIH-11042259 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals undergoing MRI scans for neurological assessments or research purposes.
✕Less likely to help
Patients who are not undergoing MRI scans or those with conditions that do not require imaging may not benefit from this research.
What this research studies
This research focuses on enhancing the quality of MRI images used to study brain structure and function by employing artificial intelligence (AI) techniques. It aims to develop tools that can automatically assess the quality of MRI images and correct any artifacts that may arise from factors like motion or magnetic field inconsistencies. By utilizing deep learning, the project seeks to streamline the image quality assessment process, making it faster and more accurate than traditional methods. This will ultimately help ensure that only high-quality images are used in large-scale studies, reducing the risk of erroneous conclusions.
Why it matters
Potential benefit: If successful, this research could lead to more reliable MRI results, improving diagnosis and treatment planning for patients with brain-related conditions.
How similar studies have gone: Other research has shown promising results in using AI for image quality enhancement, indicating that this approach has potential for success.
Where this research is happening
-
Univ of North Carolina Chapel Hill
Chapel Hill, United States Active
Researchers
- Principal investigator: Yap, Pew-Thian — Univ of North Carolina Chapel Hill
- Study coordinator: Yap, Pew-Thian
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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.