# Dynamic Imaging with Sparse Total-Body Positron Emission Tomography

> **NIH EB R03** · UNIVERSITY OF CALIFORNIA AT DAVIS · 2026 · $161,000

## Abstract

Abstract
Total-body (TB) Positron Emission Tomography (PET) is a medical imaging technology that
enables comprehensive imaging of metabolic processes across the entire body in a single
bed position. It offers substantial advantages for dynamic imaging and kinetic modeling,
such as high temporal resolution and multiparametric capabilities, making it invaluable for
assessing cancer staging, early treatment response, and distinguishing malignancy from
infection/inflammation. However, the widespread adoption of TB PET is limited by its high
cost, primarily due to the expense of scintillator material and readout electronics. This
proposal seeks to develop an affordable dynamic TB PET system through the use of sparse
detector architectures, which will reduce material costs while maintaining the performance
benefits of full-body coverage. We hypothesize that sparse designs can produce high-quality
dynamic images that allow for kinetic modeling and parametric imaging with minimal loss of
quantitative accuracy compared to full-rank TB PET. Our innovative approach combines
spatial down sampling of the detector matrix of the 2-m long EXPLORER TB PET scanner with
advanced reconstruction algorithms. Classical and deep learning-based Kernel methods
will help recover image quality in high-noise, low-count environments. This project will
address three specific aims: (1) demonstrating parametric imaging using sparse TB PET, (2)
comparing image quality between sparse TB PET and conventional PET scanners, and (3)
optimizing reconstruction parameters and detector matrix configurations for high temporal
resolution in dynamic imaging. By achieving these aims, we will develop a robust, cost-
effective TB PET system with potential to revolutionize both clinical and research
applications. The successful implementation of this technology will expand access to
dynamic TB PET, enhance diagnostic workflows, and accelerate research in cancer and
other diseases.

## Key facts

- **NIH application ID:** 11288835
- **Project number:** 1R03EB038661-01
- **Recipient organization:** UNIVERSITY OF CALIFORNIA AT DAVIS
- **Principal Investigator:** Reimund  Bayerlein
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** EB
- **Fiscal year:** 2026
- **Award amount:** $161,000
- **Award type:** 1
- **Project period:** 2026-05-01T00:00:00 → 2028-04-30T00:00:00

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/11288835

## Citation

> US National Institutes of Health, RePORTER application 11288835, Dynamic Imaging with Sparse Total-Body Positron Emission Tomography (1R03EB038661-01). Retrieved via AI Analytics 2026-06-30 from https://api.ai-analytics.org/grant/nih/11288835. Licensed CC0.

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