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

同步多示踪剂正电子发射断层扫描用于探查神经系统疾病的分子通路

基本信息

  • 批准号:
    10607431
  • 负责人:
  • 金额:
    $ 5.72万
  • 依托单位:
  • 依托单位国家:
    美国
  • 项目类别:
  • 财政年份:
    2023
  • 资助国家:
    美国
  • 起止时间:
    2023-03-23 至 2026-03-22
  • 项目状态:
    未结题

项目摘要

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.
项目总结/摘要 正电子发射断层扫描(PET)是一种采用正电子发射断层扫描(PET)的医学成像方法。 放射性核素附着在探针分子(示踪剂)上,用于非侵入性地询问体内生物过程。 每个放射性核素衰变都会释放出一个正电子,然后与一个电子结合,产生两个相反的电子。 定向共线511 keV湮灭光子。这些湮灭光子在相对的元素中被探测到 形成响应线(LOR)的光子检测器环的一部分,每个正电子发射都沿着响应线(LOR)沿着产生。后 收集数百万个这样的光子对事件并将它们沿着系统LOR定位,可以 重建以可视化和量化示踪剂探针分子在体内的3D分布。截至目前为止, PET系统每次研究仅检测一种示踪剂。然而,更全面地了解疾病生物学 通常需要同时研究多个生物过程。阿尔茨海默氏症具体是 其特征在于存在神经炎症、β-淀粉样蛋白、磷-τ和神经变性。这个项目 旨在通过策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, 发射出伽马光子和正电子这些伽马光子可以区分为 通过由光子检测器测量的它们的更高能量来检测湮灭光子。因此,LOR可以是 当高能光子几乎同时到达时, 511千电子伏的光子 与使用瞬发伽马发射体相关的挑战来自于探测到辐射和辐射的可能性较低。 在适当的定时和能量窗口内的湮灭光子和伽马光子。另一个挑战 与使用多个示踪剂相关的是由于遗漏的组织, 散射或随机光子检测。低检测效率和误分类会降低图像质量 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。 该项目提出开发和表征一种位置敏感的端盖探测器, 现有PET环系统的末端,以通过增加 系统光子探测器的立体角覆盖范围。还将采用信号处理算法 使用多个时间和能量窗口来减轻来自这两个时间和能量窗口的光子的错误分类 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之外,这些 技术将包括使用延迟的时间窗口来估计不同的随机符合率和联合 基于系统几何形状的重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精确地分离所获得的图像。通过 该项目旨在开发第一个能够同时进行多示踪剂PET成像的系统。

项目成果

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