Faster MRI to find early brain changes in Alzheimer's
Deep-Learning-Augmented Quantitative Gradient Recalled Echo (DLA-qGRE) MRI for in vivo Clinical Evaluation of Brain Microstructural Neurodegeneration in Alzheimer Disease
['FUNDING_R01'] · WASHINGTON UNIVERSITY · NIH-11456934
This project uses AI to speed up a special MRI so doctors can find small areas of brain tissue loss linked to early or preclinical Alzheimer's.
Quick facts
| Phase | ['FUNDING_R01'] |
|---|---|
| Study type | Nih_funding |
| Sex | All |
| Sponsor | WASHINGTON UNIVERSITY (nih funded) |
| Locations | 1 site (SAINT LOUIS, UNITED STATES) |
| Trial ID | NIH-11456934 on ClinicalTrials.gov |
What this research studies
From my perspective as a patient, the team combines a sensitive MRI method (qGRE) that can mark regions of lost neurons called “Dark Matter” with a deep-learning tool (RARE) to remove artifacts and speed analysis. They will train the AI using data from well-characterized participants, including people who have amyloid changes but no symptoms and those with early cognitive problems. Scans are designed to run on standard clinical MRI machines while the AI shortens analysis from hours to clinically useful times so results could be returned faster. The goal is to make a quantitative MRI marker usable for clinic visits and as an outcome measure in drug trials.
Who could benefit from this research
Good fit: Ideal candidates would include people who are amyloid-positive but without symptoms, those with mild cognitive complaints, or people with early diagnosed Alzheimer’s willing to have MRI scans.
Not a fit: People with advanced, late-stage Alzheimer's or those who cannot undergo MRI (for example due to incompatible implants or severe claustrophobia) are unlikely to benefit from participation.
Why it matters
Potential benefit: If successful, this could let people learn about Alzheimer's-related brain changes much earlier and help track disease progression or response to therapy using routine MRI.
How similar studies have performed: Prior qGRE research has identified “Dark Matter” and linked it to future decline, but pairing qGRE with deep-learning acceleration for rapid clinical use is a newer approach that remains under testing.
Where this research is happening
SAINT LOUIS, UNITED STATES
- WASHINGTON UNIVERSITY — SAINT LOUIS, UNITED STATES (ACTIVE)
Researchers
- Principal investigator: YABLONSKIY, DMITRIY A — WASHINGTON UNIVERSITY
- Study coordinator: YABLONSKIY, DMITRIY A
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.
Conditions: Alzheimer disease dementia, Alzheimer syndrome, Alzheimer's Disease