Data Driven Background Estimation in PET Using Event Energy Information

使用事件能量信息进行 PET 中数据驱动的背景估计

• 批准号: 10644161
• 负责人: Suleman Surti
• 金额: $ 57.86万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10644161
• 关键词: 90Y; Algorithms; Archives; Area; Beds; Biological Markers; Brain; Breast; Calibration; Cancer Patient; Cardiac; Clinical; Clinical Data; Clinical Research; Data; Development; Disadvantaged; Disease; Distributional Activity; Dose; Environment; Estimation Techniques; Evaluation; Event; Geometry; Goals; Image; Isotopes; Knowledge; Magnetic Resonance Imaging; Maps; Measurement; Measures; Methodology; Methods; Modeling; Molecular Target; Monitor; Monte Carlo Method; Motion; Oncology; Organ; PET/CT scan; Patient imaging; Patients; Performance; Photons; Play; Positron; Positron-Emission Tomography; Protocols documentation; Radioisotopes; Resources; Role; Scanning; Shapes; Tail; Techniques; Testing; Tracer; Work; X-Ray Computed Tomography; attenuation; base; breast scanner; clinical imaging; data acquisition; fluorodeoxyglucose; heart imaging; image reconstruction; imaging biomarker; improved; precision medicine; precision oncology; prototype; quantitative imaging; simulation; spectral energy; targeted cancer therapy; targeted treatment; transmission process; treatment response; tumor; uptake

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.

本项目的目标是开发基于能量的背景估计方法， 应用于临床数据，并在具有挑战性的成像中生成准确的定量PET图像 在低收集计数、高多次散射和即时伽马污染等情况下， 成像非标准PET同位素。我们的目标是提高PET成像的准确性， 其中现有技术的散射估计技术在准确性方面受到限制或性能较差。在这 建议，我们开发了一个数据驱动的散射估计方法，充分利用湮灭 提供光子能量信息以估计散射。该方法还被扩展以提供 校正从一些非标准PET收集的数据中存在的瞬发伽马引起的偏差 同位素我们实现，优化，并评估该算法的测量数据从临床PET 标准和非标准同位素扫描仪，并随后将该方法应用于器官- 特定扫描仪（大脑和乳房）。 拟议的工作将通过以下具体目标来完成： 评估EB方法的散射估计，（ii）EB方法的扩展，以纠正 在从非标准PET同位素获取的数据中存在的瞬发伽马污染，以及（iii） 将EB方法应用于专用的脑部和乳房PET扫描仪几何形状。 除了其在低信噪比的情况下优于现有的散射估计方法之外， 所收集的计数和/或数据具有更高水平的多重散射，预期所提出的技术 更快，不需要知道成像视场外的活动分布， 不需要透射或CT图像。这项技术的成功演示可能会大大 扩大定量PET/CT在肿瘤学领域的应用，如低剂量的治疗监测， 剂量重复PET扫描，使用低正电子产额放射性核素（例如124 I， 86 Y等），或以高计数率采集数据（如在心脏成像或用124 I或86 Y成像中）。超出 此外，它还将在心脏研究中提供改进的定量（82 Rb，13 NH3或11 C-乙酸盐）。 由于所提出的散射估计方法不需要CT图像，因此其可以在以下方面具有应用： PET/MR成像以及具有一些患者运动的临床研究-这两种情况下， 图像要么不可用，要么受损，导致传统估计方法中的错误 散开