# Optimization of PET Image Reconstruction for Lesion Detection

> **NIH NIH R03** · MASSACHUSETTS GENERAL HOSPITAL · 2021 · $89,563

## Abstract

Optimization of PET Image Reconstruction for Lesion Detection
Abstract
PET is a molecular imaging modality widely used in oncology studies due to its high sensitivity and the
potential of early diagnosis. For neuroendocrine tumors (NETs), 68Ga-DOTATATE PET has been
recently used in clinical routine for imaging NETs in adult and pediatric patients since 2016. It plays an
important role in the diagnosis and staging of NETs. However, compared to 18F-FDG PET, the image
quality of 68Ga-DOTATATE PET is lower due to much larger positron range, shorter half-life, and lower
dose administration limited by generator capacity. All of these compromises the lesion detectability of
68Ga-DOTATATE PET, especially for small lesions, and can potentially lead to inaccurate NET
diagnosis. As 68Ga-DOTATATE PET is increasingly used in clinics, there is an urgent and unmet need
to further optimize 68Ga-DOTATATE PET/CT imaging for NET detection. Recently, data-driven
methods have been developed for PET image denoising, where the PET system model is not
considered. As the tumor-to-background ratio of 68Ga-DOTATATE PET is greater than 18F-FDG PET,
the lesion recovery of 68Ga-DOTATATE PET can be hugely influenced by the smoothing effects as well
as potential mismatches between training and testing datasets. In this study, we propose a novel data-
informed and lesion detection-driven image reconstruction framework. The PET system model, image
denoising module, and lesion-detection module will all be included in this reconstruction framework.
The two specific aims of this exploratory proposal are (1) to develop a lesion detection-driven PET
image reconstruction framework and validate it based on comprehensive computer simulations, (2) to
apply the proposed reconstruction framework to existing clinical 68Ga-DOTATATE PET/CT datasets
and test it based on various figure-of-merits. We expect that the integrated outcome of the specific aims
will be a novel and robust image reconstruction framework to better recover lesions in a 68Ga-
DOTATATE PET scan, which is essential for NET managements.

## Key facts

- **NIH application ID:** 10206141
- **Project number:** 5R03EB030280-02
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** Kuang Gong
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $89,563
- **Award type:** 5
- **Project period:** 2020-07-01 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10206141

## Citation

> US National Institutes of Health, RePORTER application 10206141, Optimization of PET Image Reconstruction for Lesion Detection (5R03EB030280-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10206141. Licensed CC0.

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