# Data Driven Background Estimation in PET Using Event Energy Information

> **NIH NIH R56** · UNIVERSITY OF PENNSYLVANIA · 2022 · $578,570

## Abstract

The objective of this project is to develop methodology for energy-based background estimation that can
be applied to clinical data and produce accurate quantitative PET images over challenging imaging
situations such as low collected counts, high multiple scatter, and prompt gamma contamination when
imaging non-standard PET isotopes. The goal is to enhance the accuracy of PET imaging in situations
where current state-of-art scatter estimation techniques are limited in accuracy or perform poorly. In this
proposal, we develop a data driven scatter estimation methodology that makes full use of the annihilation
photon energy information present to estimate scatter. This method is also extended to provide
correction for bias arising from prompt gammas present in data collected form some non-standard PET
isotopes. We implement, optimize, and evaluate this algorithm on measured data from a clinical PET
scanner for standard and non-standard isotopes, and subsequently apply the methodology to organ-
specific scanners (brain and breast).
 The proposed work will be accomplished through the following specific aims: (i) optimization and
evaluation of the EB method for scatter estimation, (ii) extension of the EB methodology to correct for
prompt gamma contamination present in data acquired from non-standard PET isotopes, and (iii)
application of the EB methodology to dedicated brain and breast PET scanner geometries.
 In addition to its advantages over existing scatter estimation methodology in situations with low
collected counts and/or data with higher level of multiple scatter, the proposed technique is expected to
be faster, does not require knowledge of activity distribution outside the imaging field-of-view, and does
not require a transmission or CT image. Successful demonstration of this technique may significantly
expand the application of quantitative PET/CT in oncology areas such as treatment monitoring with low-
dose repeat PET scans, imaging with new biomarkers that use low positron yield radionuclides (e.g. 124I,
86Y, etc.), or acquiring data at high count-rates (as in cardiac imaging or imaging with 124I or 86Y). Beyond
oncology, it will also provide improved quantitation in cardiac studies (82Rb, 13NH3, or 11C-actetate).
Since, the proposed scatter estimation method does not require a CT image it may have an application in
PET/MR imaging as well as clinical studies with some patient motion – both situations where the CT
image is either not available or is compromised leading to errors in the traditional way of estimating
scatter.

## Key facts

- **NIH application ID:** 10644161
- **Project number:** 1R56EB033585-01
- **Recipient organization:** UNIVERSITY OF PENNSYLVANIA
- **Principal Investigator:** Suleman Surti
- **Activity code:** R56 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $578,570
- **Award type:** 1
- **Project period:** 2022-09-01 → 2024-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10644161

## Citation

> US National Institutes of Health, RePORTER application 10644161, Data Driven Background Estimation in PET Using Event Energy Information (1R56EB033585-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10644161. Licensed CC0.

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