Improving brain imaging accuracy using deep learning techniques
Official title: Distortion Correction in Functional MRI with Deep Learning
Funded at University of Illinois at Chicago · NIH-10827513 · Data updated 2026-11-12
This study is working on a new way to make brain scans more accurate by using advanced computer techniques to fix any distortions in the images, which could help doctors better understand brain activity and improve care for patients.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Illinois at Chicago NIH-funded |
| Lab location | 1 site (Chicago, UNITED STATES) |
| Project start | May 1, 2023 |
| Project end | April 30, 2026 |
| Funding year | 2024 |
| Award (this year) | $79,950 |
| NIH project number | 5R03EB034480-02 |
| Project ID | NIH-10827513 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals undergoing fMRI scans for neurological assessments or cognitive neuroscience studies.
✕Less likely to help
Patients who are not undergoing fMRI scans or those with conditions that do not require brain imaging may not benefit from this research.
What this research studies
This research focuses on enhancing the accuracy of functional magnetic resonance imaging (fMRI), a technique used to map brain activity. It addresses the issue of geometric distortions caused by magnetic field inhomogeneities, which can lead to errors in brain imaging. By employing a novel deep learning approach, the project aims to correct these distortions directly from the raw imaging data, improving the quality of brain images without relying on traditional methods that can introduce artifacts. This could lead to more precise detection of brain activity, benefiting both cognitive neuroscience and patient care.
Why it matters
Potential benefit: If successful, this research could significantly improve the accuracy of brain imaging, leading to better diagnosis and treatment planning for neurological conditions.
How similar studies have gone: Other research has shown promise in using deep learning techniques for image correction, suggesting that this approach could be effective.
Where this research is happening
-
University of Illinois at Chicago
Chicago, United States Active
Researchers
- Principal investigator: Luo, Qingfei — University of Illinois at Chicago
- Study coordinator: Luo, Qingfei
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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.