# Next Generation Brain PET Imaging

> **NIH NIH R01** · STANFORD UNIVERSITY · 2021 · $564,570

## Abstract

Abstract
Gold-standard quantitative imaging studies are often difficult to implement,
limited by financial and logistical issues, or expose the patient to unnecessary
risks. Deep learning has shown great promise in recent years for many medical
applications; one use is to synthesize improved images. Such image trans-
formation methods offer the potential to improve the quality, value, and
accessibility of medical imaging.
The goal of this project is to develop deep convolutional neural network
approaches to FDG PET imaging, the most commonly performed clinical brain-
focused PET study in the USA. Using simultaneous PET/MRI, we will train
networks to produce diagnostic PET images from ultra-low dose PET and MR
images. We will explore the three reimbursed clinical indications for this
imaging modality (tumor recurrence, dementia, and epilepsy) using both
quantitative metrics and repeated reader studies to assess equivalence and
evaluate possible AI generalization bias related to simultaneity, scanner type,
age, gender, and disease prevalence.
Next, we will evaluate whether we can move beyond ultra-low dose and remove
the radiation dose altogether, synthesizing FDG brain PET images from MR
inputs only, relying on the information in multi-modal functional MRI. Finally,
we will assess whether we can use deep networks to combine imaging and non-
imaging data such as clinical and genetic information to further improve image
transformation and predict future images and image-based biomarkers.
Significantly reducing or even eliminating the need for radiation to produce
brain FDG PET images would be truly transformative while the ability to
predict the future will enable personalized radiology and enhance our ability to
perform clinical trials.

## Key facts

- **NIH application ID:** 10279862
- **Project number:** 1R01NS123025-01
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Gregory George Zaharchuk
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $564,570
- **Award type:** 1
- **Project period:** 2021-09-01 → 2026-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10279862

## Citation

> US National Institutes of Health, RePORTER application 10279862, Next Generation Brain PET Imaging (1R01NS123025-01). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10279862. Licensed CC0.

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