# Deep Learning Methods for Improving Gallium 68-Based PET Imaging

> **NIH NIH R01** · UNIVERSITY OF FLORIDA · 2024 · $442,037

## Abstract

Abstract
Neuroendocrine tumors (NETs) are a heterogeneous group of tumors with increasing incidence, which are hard
to identify in the early stage and repeatedly misdiagnosed, yielding 20%-50% of patients with distant metastases
at initial diagnosis. Prostate cancer (PCa) is the most common solid-organ malignancy in men in the United
States. Distinguishing indolent from aggressive PCa and differentiation of localized disease and metastatic
spread are essential for PCa treatment selection. For both NETs and PCa, disease-specific overexpression
exists in cancer cells. 68Ga-DOTATATE and 68Ga-PSMA-11, the two most widely used Gallium-68 PET tracers,
can target the overexpression in cancer cells of NETs and PCa, respectively. They are essential imaging
techniques for NETs and PCa management, given their high sensitivity and specificity in detecting primary tumor
and metastatic spread. Due to the shorter half-life and larger positron range of Gallium 68 and the lower injection
dose limited by generator capacity, Gallium-68 PET has a lower image quality compared with 18F-FDG PET,
which significantly compromises its lesion detectability and quantification accuracy. As Gallium-68 PET is
increasingly adopted in clinics, there are unmet needs to further optimize it for better disease management. The
goal of this project is to improve Gallium 68-based PET image quality through deep learning (DL)-based motion
correction, image reconstruction, and kinetic modeling. Aim 1 of this project is to develop a data-driven
respiratory motion-correction framework with phase-matched attenuation correction. Aim 2 of this project is to
develop a DL-based image reconstruction method to improve static Gallium 68-based PET imaging. Aim 3 of
this project is to further develop DL-based kinetic-modeling methods to improve dynamic Gallium 68-based PET
imaging. Aim 4 of this study is to perform comprehensive clinical evaluations of the developed methods. We
expect that the integrated outcome of the four aims will be novel, effective, and robust motion correction and
reconstruction methods for Gallium-68 PET that can improve its lesion detectability and quantification accuracy.

## Key facts

- **NIH application ID:** 10979633
- **Project number:** 1R01EB034692-01A1
- **Recipient organization:** UNIVERSITY OF FLORIDA
- **Principal Investigator:** Kuang Gong
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $442,037
- **Award type:** 1
- **Project period:** 2024-07-15 → 2028-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10979633

## Citation

> US National Institutes of Health, RePORTER application 10979633, Deep Learning Methods for Improving Gallium 68-Based PET Imaging (1R01EB034692-01A1). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10979633. Licensed CC0.

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