Data Driven Background Estimation in PET Using Event Energy Information

该项目目标是开发基于能量的背景估计方法，该方法可以 应用于临床数据，并在具有挑战性的成像上产生准确的定量PET图像 在以下情况下，会出现采集计数低、多次散射高、伽马污染迅速等情况 成像非标准的PET同位素。其目标是在某些情况下提高PET成像的准确性 其中当前最先进的散布估计技术在准确性上有限或执行得很差。在这 我们提出了一种数据驱动的散布估计方法，该方法充分利用了湮灭效应 存在用于估计散射的光子能量信息。此方法还被扩展为提供 对某些非标准PET采集数据中存在的瞬发伽马的偏差的校正 同位素。我们在临床PET的测量数据上实现、优化和评估了该算法。 标准和非标准同位素的扫描仪，并随后将该方法应用于器官- 特定的扫描仪(大脑和乳房)。 拟议的工作将通过以下具体目标来完成：(1)优化和 对用于散点估计的EB方法的评价，(2)扩展EB方法以校正 从非标准PET同位素获得的数据中存在即时伽马污染，以及(3) EB方法在专用脑和乳房PET扫描仪几何形状中的应用。 除了与现有散布估计方法相比，它在低成本情况下的优势之外 收集的计数和/或具有更高水平的多次散射的数据，所提出的技术预计将 速度更快，不需要了解成像视场外的活动分布，并且 不需要传输或CT图像。这项技术的成功演示可能会显著 拓展定量PET/CT在肿瘤治疗监测等领域的应用 剂量重复PET扫描，使用使用低正电子产额放射性核素(例如，124I， 86Y等)，或以高计数速率采集数据(如在心脏成像或124I或86Y成像中)。超越 在肿瘤学方面，它还将在心脏研究中提供更好的定量(82Rb、13NH3或11C-醋酸酯)。 由于所提出的散度估计方法不需要CT图像，因此它可能在 PET/MR成像以及有一些患者运动的临床研究-这两种情况下CT 图像不可用或被破坏，导致传统估计方式出现错误 散开。

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.