Simultaneous Multi-Tracer Positron Emission Tomography for Interrogating Molecular Pathways of Neurological Disorders

项目摘要/摘要 正电子发射断层扫描(PET)是一种利用正电子发射的医学成像方法 附着在探测分子(示踪剂)上的放射性核素，用于非侵入性地询问体内的生物过程。 每一次放射性核素衰变都发射一个正电子，然后正电子与一个电子结合，产生两个相反的电子。 定向、共线511keV湮没光子。这些湮没光子在相反的元素中被探测到 形成响应线(LOR)的光子探测器环，每个正电子发射都沿着该响应线发出。之后 收集数百万个这样的光子对事件并沿着系统LOR定位它们，图像可以 重建以可视化和量化示踪探针分子在体内的3D分布。直到现在, 宠物系统每项研究只检测到一个示踪剂。然而，对疾病生物学的更完整的了解 通常需要同时研究多个生物过程。阿尔茨海默氏症特别是 以神经炎症、β-淀粉样蛋白、磷酸-τ和神经变性为特征。这个项目 旨在通过战略性地选择至少一种PET示踪剂来实现多个示踪剂的同时成像 发射伽马光子与其正电子级联。这些伽马光子可以与 通过光子探测器测量的较高能量湮没光子。因此，LORS可以被 与正电子+伽马示踪剂有关，当一个高能光子几乎同时到达时 511keV的光子。 与使用即时伽马发射器相关的挑战来自于检测到 在适当的时间和能量窗口内湮没光子和伽马光子。另一个挑战 与使用多个示踪剂相关的是由于遗漏的组织而导致示踪剂中的事件的错误分类 检测到散射的或随机的光子。检测效率低和分类错误会降低图像质量 和相关多示踪剂图像的准确性；因此，必须开发方法来缓解这些问题。 该项目建议开发和表征一种位置敏感的端盖探测器，该探测器将覆盖开口 结束现有的PET环系统，以通过增加 系统光子探测器的立体角度覆盖范围。还将使用信号处理算法 使用多个时间窗和能量窗来减少来自两个时间窗的光子的错误分类 除了补偿双光子发射器和三光子发射器之间的灵敏度差异外，还可以对发射器的灵敏度进行补偿。这些 技术将包括使用延迟时间窗口来估计不同的随机重合率和联合 基于系统几何的符合事件的最大似然估计。我们还将使用深度学习 以提高低剂量图像的图像质量，并准确地分离所获得的图像。穿过 在这些技术的基础上，本项目旨在开发第一个能够同时进行多示踪PET成像的系统。

Project Summary/Abstract Positron emission tomography (PET) is a method of medical imaging that employs positron emitting radionuclides attached to probe molecules (tracers) for non-invasively interrogating biological processes in vivo. Each radionuclide decay emits a positron, which then combines with an electron and creates two oppositely directed, colinear 511 keV annihilation photons. These annihilation photons are detected in opposing elements of a photon detector ring forming lines of response (LOR) along which each positron emission originated. After collecting millions of such photon pair events and positioning them along system LORs, an image can be reconstructed to visualize and quantify the 3D distribution of tracer probe molecules within the body. Up to now, PET systems detect only one tracer per study. However, more complete understanding of the disease biology often requires the study of multiple biological processes simultaneously. Alzheimer’s Disease specifically is characterized by presence of neuroinflammation, β-amyloid, phosphor-τ, and neurodegeneration. This project aims to enable simultaneous imaging of multiple tracers by strategically choosing at least one PET tracer that emits gamma photons in cascade with their positron. These gamma photons can be differentiated from annihilation photons through their higher energy measured by the photon detector. Thus, LORs can be associated with this positron + gamma tracer when a high energy photon arrives nearly the same time as a pair of 511 keV photons. Challenges associated with using prompt gamma emitters come from the lower probability of detecting both the annihilation photons and gamma photon within the appropriate timing and energy windows. Another challenge associated with using multiple tracers is misclassification of events among tracers due to missed, tissue scattered, or random photons detected. Low detection efficiency and misclassification will reduce image quality and accuracy of the associated multi-tracer images; thus, methods must be developed to mitigate these issues. This project proposes to develop and characterize a position-sensitive endcap detector that will cover the open end of an existing PET ring system to increase the detection efficiency for 3-photon events through increasing the solid angle coverage of the system photon detectors. Signal processing algorithms will also be employed using multiple temporal and energy windows to mitigate misclassification of photons coming from the two emitters in addition to compensating for sensitivity differences between two- and three-photon emitters. These techniques will include use of delayed time windows to estimate the different random coincidence rates and joint maximum likelihood estimation of coincidence events based on system geometry. We will also use deep learning to improve the image quality of the low dose images and to accurately separate the images obtained. Through these techniques, this project aims to develop the first system capable of simultaneous multi-tracer PET imaging.