Standardizing amyloid and tau PET brain scans
Official title: Tracer harmonization for amyloid and tau PET imaging using statistical and deep learning techniques
Funded at Banner Health · NIH-11297529 · Data updated 2026-11-12
The team will create methods to make amyloid and tau PET brain scans more comparable so people being checked for Alzheimer’s get clearer, more consistent results.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | Banner Health NIH-funded |
| Lab location | 1 site (Phoenix, United States) |
| Project start | August 1, 2022 |
| Project end | July 31, 2027 |
| Funding year | 2025 |
| Award (this year) | $704,505 |
| NIH project number | 4R01AG073424-02 |
| Project ID | NIH-11297529 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal participants are adults who have had or will have amyloid or tau PET scans, including people with memory concerns, mild cognitive impairment, or early Alzheimer’s disease.
✕Less likely to help
People without access to amyloid or tau PET imaging, or with dementia types unrelated to amyloid/tau pathology, may not see direct benefit from this work.
What this research studies
Researchers will use existing amyloid and tau PET images from different tracers and centers and apply advanced statistical methods and deep-learning models to make images and measurements more compatible. They will refine image-processing steps such as partial volume correction and develop new quantification strategies that leverage full imaging information. The project aims to produce harmonized measurements and a common threshold for 'positivity' that works across datasets. If successful, these methods will let doctors and researchers combine scans from many sources for clearer diagnosis and research.
Why it matters
Potential benefit: This could lead to more reliable PET results across centers, helping doctors diagnose Alzheimer’s earlier and enroll the right people into trials.
How similar studies have gone: Prior harmonization techniques and partial volume corrections have shown promise, but integrating advanced statistics with deep learning to generalize across many tracers is relatively new.
Where this research is happening
-
Banner Health
Phoenix, United States Active
Researchers
- Principal investigator: Su, Yi — Banner Health
- Study coordinator: Su, Yi
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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.