Improving MRI image quality using advanced AI techniques
Reliable AI for Medical Image Reconstruction
This study is working on new computer programs to make MRI scans faster and clearer, which could help patients get better diagnoses and treatments.
Quick facts
| Grant type | NIH-funded research |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Southern California NIH-funded |
| Lab location | 1 site (Los Angeles, UNITED STATES) |
| Project ID | NIH-10687707 on NIH RePORTER |
What this research studies
This research focuses on developing new deep learning algorithms to enhance the quality and speed of Magnetic Resonance Imaging (MRI). By addressing challenges such as low-quality training data and motion artifacts, the project aims to create a reliable toolkit for MRI reconstruction that can be used across multiple hospitals. Patients may benefit from faster and more accurate MRI scans, which could lead to better diagnosis and treatment outcomes. The research also involves collecting and sharing new datasets to foster further advancements in medical imaging.
Who could benefit from this research
Good fit: Ideal candidates for this research are individuals requiring MRI scans, particularly those with conditions affecting the musculoskeletal system.
Not a fit: Patients who do not require MRI imaging or those with conditions that do not involve musculoskeletal issues may not benefit from this research.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate MRI scans for patients.
How similar studies have performed: Other research has shown promise in using AI for medical imaging, indicating that this approach could lead to significant advancements in the field.
Where this research is happening
Los Angeles, UNITED STATES
- University of Southern California — Los Angeles, United States (Active)
Researchers
- Principal investigator: Soltanolkotabi, Mahdi — University of Southern California
- Study coordinator: Soltanolkotabi, Mahdi
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.