# Quantitative Low-Dose PET Imaging

> **NIH NIH R01** · YALE UNIVERSITY · 2020 · $679,852

## Abstract

Project summary
Quantitative PET has become increasingly important in clinical management and research, in particular for
predicting and assessing response to therapy for cancer patients. Current PET protocols involve injection of
PET tracers that typically result in ~6-7 mSv radiation dose to patients. For patients who require multiple
repeated PET scans to monitor the response to therapy, and for patients who need PET scans with two or
more tracers (e.g., FDG + FLT) to optimally predict response to therapy, it is critical to reduce the radiation
dose from the PET tracer injection, while still maintaining the quantitative accuracy and image quality for
cancer management. When reducing injection dose, the PET images will have higher noise due to fewer
detected counts, which will subsequently introduce errors in quantitative measurements. For moving organs
and tumors such as those in the lung and abdomen, respiratory motion can substantially degrade quantitative
accuracy, so motion correction is required. Conventional motion correction uses a gating strategy that rebins
the PET data, resulting in substantially higher noise in each gate. More advanced methods incorporate motion
vector estimation in the image domain for post-registration or motion compensated image reconstruction using
all detected events without increasing noise. The motion vectors need to be derived from gated PET, which are
even noisier when using a reduced tracer injection in low-dose studies, imposing substantial challenges for
accurate and reliable voxel-by-voxel motion vector estimation. In dynamic PET studies with clinical cardiac
tracers and other novel oncology and neurology tracers, quantification is even more challenging for low-dose
PET as each dynamic frame only contains a small fraction of detected events so the high image noise will
affect the determination of image-derived input functions and can lead to bias and high noise in parametric
images. In this project, to reduce image noise and maintain quantitative accuracy in PET, we propose to
develop, optimize, and evaluate multiple innovative imaging methods for low-dose PET data to achieve
comparable quantitative accuracy as full-dose PET. While the imaging developments are generally applicable
to all PET tracers in oncology, neurology, and cardiology, since cancer is the primary clinical application of
PET, we will focus our investigation and optimization in this project on three lung cancer imaging tracers at
different clinical adoption stages as examples: 1) 18F-FDG as a routine clinical tracer, 2) 18F-FMISO for hypoxia
studies as a tracer for human research, and 3) 18F-PD-L1 that specifically binds to human PD-L1 in tumors and
other organs as a recent first-in-human tracer. For each tracer, we will investigate 1) static PET, 2) gated and
respiratory motion corrected PET, and 3) dynamic PET.

## Key facts

- **NIH application ID:** 9924591
- **Project number:** 5R01EB025468-03
- **Recipient organization:** YALE UNIVERSITY
- **Principal Investigator:** Richard E. Carson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $679,852
- **Award type:** 5
- **Project period:** 2018-07-24 → 2023-04-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9924591

## Citation

> US National Institutes of Health, RePORTER application 9924591, Quantitative Low-Dose PET Imaging (5R01EB025468-03). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9924591. Licensed CC0.

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