# Optimization of Clinical and Research PET Imaging

> **NIH NIH R01** · UNIVERSITY OF PENNSYLVANIA · 2022 · $495,461

## Abstract

The goal of this project is to quantitatively demonstrate improved clinical performance of the latest
PET/CT systems using task-specific image quality measures in realistic patient studies These results will
be correlated to the performance of simpler metrics that are easier to calculate. Our objective is to either
guide a reduction in injected activity or imaging time on these latest systems without sacrificing existing
clinical capabilities or to demonstrate new clinical capabilities. The evaluation will be performed as a
function of scanner design, image reconstruction, and patient habitus in order to maintain good clinical
performance over all patients. Currently, PET imaging protocols are designed based on imaging at or
near the peak noise equivalent counts (NEC). While the performance of these scanners has improved,
the patient injection protocols have remained constant while the imaging time is reduced. Also, while
NEC gives an estimate of the general pixel signal-to-noise ratio (SNR), its relation to clinically relevant
metrics is not obvious especially since it does not include the effects of spatial and TOF resolution, and
image reconstruction. In this project we perform a clinically realistic study replicating two tasks in
oncology: (i) lesion detection and localization common in diagnostic PET, and (ii) monitoring tumor
response to therapy with multiple scans. Our embed lesions in clinical patient data prior to image
reconstruction, thereby providing a ground-truth for comparison. The proposed work is accomplished
through the following specific aims: (1) Develop and evaluate a new simplified NEC metric for optimizing
PET images for clinically relevant tasks that includes spatial and TOF resolution effects and test its
correlation to a task-specific metric, (2) Perform a comprehensive human observer study for lesion
detection and localization, and (3) Perform a ROC study that models tumor response to therapy. In the
short term, the results of this work will quantitatively demonstrate improved clinical performance with the
latest PET/CT (commercial systems as well as PennPET Explorer scanner), thereby allowing shorter
imaging times and/or reduced injected activity. Correlation of the task-specific metric results to simplified
imaging metrics will also provide an important tool for future PET system design and image
reconstruction evaluation and optimization where complex, task-specific evaluations will not be practical.
Clinically, demonstration of good performance with very short scan times will open the possibility of
breath-hold imaging with no motion artifacts. Our results will also enable an expansion of PET imaging
into new clinical areas where patient radiation exposure is a limiting concern in the use of this molecular
imaging modality. We envision serial patient imaging for monitoring treatment response, as well as
screening/surveillance studies in high-risk patients, 2 areas of immediate relevance.

## Key facts

- **NIH application ID:** 10322759
- **Project number:** 5R01EB028764-03
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Suleman Surti
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $495,461
- **Award type:** 5
- **Project period:** 2020-04-01 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10322759

## Citation

> US National Institutes of Health, RePORTER application 10322759, Optimization of Clinical and Research PET Imaging (5R01EB028764-03). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10322759. Licensed CC0.

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