# Generalizable Deep Learning Networks for Dual-tracer Amyloid/Tau PET/MRI Imaging of Alzheimer's Disease

> **NIH NIH K99** · STANFORD UNIVERSITY · 2021 · $100,863

## Abstract

Project Summary
 Alzheimer’s Disease (AD) is a devastating neurodegenerative disorder and a major public health crisis,
currently affecting over 5.8 million Americans and expected to rise as the population ages. Positron emission
tomography (PET) imaging can identify the hallmark proteinopathies of AD, including amyloid protein plaques
and neurofibrillary tangles (composed primarily of tau protein) accumulating in the brain. While there is evident
need for more PET neuroimaging, for example, to elucidate the sequence of amyloid and tau deposition in
preclinical AD, its increased utility in longitudinal imaging studies with large study populations is limited by
recruitment and cost. In particular, making multiple visits to the scanning site will be difficult for participants
living far away, and the high cost of injected radiotracers will limit the scalability of PET studies.
 In this project we propose using deep learning-based convolutional neural networks (CNNs) to enhance
ultra-low-dose amyloid and tau PET for imaging AD. Our specific aims are (1) to validate the diagnostic value
of the CNNs in actual ultra-low-dose amyloid and tau imaging sessions, with the injected dose as low as 1% of
the original, and with actual ultra-low-dose data, to validate simulations for use in subsequent aims and future
studies; (2) to apply the ultra-low-dose CNN to data collected on other PET systems and tracers, in order to
demonstrate the CNN’s generalizability; and (3) to evaluate the value of deep learning-aided ultra-low-dose
amyloid and tau PET for tracking cognitive decline in a preclinical AD population.
 The innovation of this work lies in using multimodal imaging in addition to advanced machine learning
techniques to enable acquisition of diagnostic-level PET images at extremely low dose levels. Performing
actual ultra-low-dose PET acquisitions is also highly novel in itself. The outcome of this proposal is removing
the limiting factors to large-scale clinical longitudinal imaging, shortening acquisitions spanning multiple days
and visits to several hours in one visit with a successive ultra-low-dose and full-dose dual-tracer scan protocol.
Significant dose reduction can also be achieved, allowing for more frequent amyloid/tau PET scanning. This
flexibility will not only increase the utility of PET, aid longitudinal studies in dementia, but enable future
comprehensive imaging of multiple PET-based biomarkers as these tracers are being developed.

## Key facts

- **NIH application ID:** 10214874
- **Project number:** 1K99AG068310-01A1
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Kevin Tze-Hsiang Chen
- **Activity code:** K99 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $100,863
- **Award type:** 1
- **Project period:** 2021-05-01 → 2021-07-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10214874

## Citation

> US National Institutes of Health, RePORTER application 10214874, Generalizable Deep Learning Networks for Dual-tracer Amyloid/Tau PET/MRI Imaging of Alzheimer's Disease (1K99AG068310-01A1). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10214874. Licensed CC0.

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