# SPECT with a Compton Camera for Thyroid Cancer Imaging

> **NIH NIH R21** · UNIVERSITY OF MASSACHUSETTS LOWELL · 2021 · $407,750

## Abstract

SPECT with a Compton Camera for Thyroid Cancer Imaging
ABSTRACT
The thyroid gland is butterfly-shaped in the lower front of the neck, and secretes hormones for normal biological
functions. The incidence of thyroid nodules increases with age, involving more than half of the population.
Thyroid cancer is the most common type of endocrine-related cancer and the most common cancer in young
women, with over 50K new cases per year in the United States. To detect and treat thyroid cancer, it is desired to
characterize the nodule accurately. Currently, single photon emission computed tomography (SPECT) and
computed tomography (CT) are used with radioiodine scintigraphy to evaluate patients with thyroid cancer. The
gamma camera for SPECT contains a mechanical collimator that greatly compromises dose efficiency and limits
diagnostic sensitivity. Fortunately, the Compton camera is emerging as an ideal approach for mapping the
distribution of radiopharmaceuticals inside the thyroid. It is because the Compton camera requires no
mechanical collimation and in principle rejects no gamma ray photon. Hence, radiation dose will be reduced by
orders of magnitude in screening and follow-up scans of patients.
In this R21 project, we will design a high-efficiency and high-quality tomographic imaging system with a Compton
camera dedicated to thyroid cancer imaging, and develop an associated software package for Compton
scattering based SPECT imaging. The major innovation lies in the deep learning empowered image
reconstruction and the Timepix3-based Compton camera for thyroid cancer imaging. The proposed techniques
help reduce radiation dose dramatically, improve the imaging speed, and enhance image quality and diagnostic
performance, having a great potential for clinical translation. The three specific aims are defined as follows: (1) a
Monte Carlo simulator will be developed for gamma ray Compton data synthesis; (b) deep reconstruction
algorithms will be developed for Compton camera based SPECT, and (c) a SPECT system will be designed in
numerical simulation and phantom experiments for ultra-low-dose thyroid imaging.
Upon the completion of this project, the simulation and reconstruction software tools should have been
developed for tomographic imaging of the radiotracer distribution in the human thyroid, and a point of care (POC)
SPECT system will have been designed with the Compton camera and experimentally verified for a superior
diagnostic performance at an ultra-low dose. The synergy among the deep learning techniques and the
cutting-edge Timepix3 camera will have been demonstrated for a follow-up R01 proposal.

## Key facts

- **NIH application ID:** 10286795
- **Project number:** 1R21CA264772-01
- **Recipient organization:** UNIVERSITY OF MASSACHUSETTS LOWELL
- **Principal Investigator:** Ge Wang
- **Activity code:** R21 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $407,750
- **Award type:** 1
- **Project period:** 2021-09-16 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10286795

## Citation

> US National Institutes of Health, RePORTER application 10286795, SPECT with a Compton Camera for Thyroid Cancer Imaging (1R21CA264772-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10286795. Licensed CC0.

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