Improving MRI speed and quality for better patient care
Official title: Optimizing Acquisition and Reconstruction of Under-sampled MRI for Signal Detection
Funded at Hofstra University · NIH-11219783 · Data updated 2026-11-12
This study is working on making MRI scans quicker and better by using new techniques, so patients can spend less time in the scanner while still getting clear and accurate images for their diagnosis.
Quick facts
| Grant type | R15 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Hofstra University NIH-funded |
| Lab location | 1 site (Hempstead, United States) |
| Project start | September 1, 2023 |
| Project end | August 31, 2026 |
| Funding year | 2023 |
| Award (this year) | $320,195 |
| NIH project number | 7R15EB029172-03 |
| Project ID | NIH-11219783 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are patients who require MRI scans for diagnostic purposes, particularly those needing timely evaluations.
✕Less likely to help
Patients who do not require MRI scans or those with conditions that do not involve subtle lesion detection may not benefit from this research.
What this research studies
This research focuses on enhancing the speed and quality of magnetic resonance imaging (MRI) by optimizing techniques that allow for faster data acquisition and reconstruction. By using advanced methods like under-sampling and deep learning, the project aims to reduce the time patients spend in the MRI scanner while ensuring that the images produced remain diagnostically accurate. The research will involve developing models to detect subtle lesions in the images, validated through studies with human observers. Ultimately, the goal is to make MRI more efficient and accessible for patients.
Why it matters
Potential benefit: If successful, this research could significantly reduce MRI scan times, leading to improved patient comfort and lower healthcare costs.
How similar studies have gone: Previous research has shown promise in using under-sampling and deep learning techniques to accelerate MRI, indicating that this approach has potential for success.
Where this research is happening
-
Hofstra University
Hempstead, United States Active
Researchers
- Principal investigator: Pineda, Angel Ramon — Hofstra University
- Study coordinator: Pineda, Angel Ramon
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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.