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) application of the EB methodology to dedicated brain and breast PET scanner geometries, and (iii) extension of the EB methodology to correct for prompt gamma contamination present in data acquired from non-standard PET isotopes. 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 will significantly impact routine oncologic imaging where heavy patients with increased scatter, reduced counts and limited imaging field-of-view will be susceptible to reduced quantitative accuracy. In addition, this technique can also expand the application of quantitative PET/CT in new oncology imaging 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.

Quantitative PET biomarker imaging promises to play a significant role in delivering precision medicine for cancer patients, since it can provide accurate biomarker uptake measurements that are necessary for tumor characterization as well as measuring changes in response to therapy. While PET images acquired during routine 18F-FDG imaging are highly accurate, it is challenging to maintain the same performance when imaging large patients (increased multiple scatter, low counts, and reduced tail-fitting region), in situations where few coincidence events are collected (e.g. imaging at low injected activity levels for repeat scans or imaging tracers with a low positron yield), or imaging new radiotracers using isotopes emitting prompt gamma. For PET to fulfill its role in this era, quantification accuracy of PET images needs to be maintained over a large range of data acquisition protocols, where the quantification challenge is the accuracy of the scatter and background estimation method that is used to compensate (correct) for the bias present in the data due to scattered coincidences and prompt gamma events.