Development of a combined Gamma/Positron system for molecular imaging of the human brain at sub-500 micron spatial resolution

开发伽玛/正电子组合系统,用于以亚 500 微米空间分辨率对人脑进行分子成像

基本信息

  • 批准号:
    10722205
  • 负责人:
  • 金额:
    $ 43.22万
  • 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:
    2023-08-18 至 2025-07-31
  • 项目状态:
    未结题

项目摘要

SUMMARY We are proposing a new approach to a hybrid imaging modality that has been called “b+g” or “pamma-positron” Imaging [Gri07] that promises to simultaneously overcome 1) the sensitivity limits of single-gamma-ray-photon emission imaging, 2) the challenge of distinguishing between two different positron-emitting isotopes, and 3) the physics-based spatial resolution limits inherent in radioisotope imaging based on detection of positron- annihilation photons alone [Lan14]. The intent is to significantly advance molecular imaging of the human brain by allowing visualization of smaller substructures, quantification of smaller amounts of radiotracer uptake, and simultaneous measurement of multiple dynamic and spatial uptake patterns in advanced multi-isotope studies of normal brain function. The required elements to make this feasible comprise i) a detector approach for annihilation and gamma-ray photons that can yield rich data for precise energy, position, and timing estimation for both photoelectric and Compton interactions, ii) processing algorithms in firmware and software to sort and make optimal use of the various combinations of signals that can occur with and without coincidence, iii) reconstruction algorithms based on likelihoods that incorporate probabilities of emission, detection, positron range, non-collinearity, and Compton kinematics, and iv) detection and compensation for attenuation and subject motion – effects that if not addressed will become limiting factors for resolution and image quality. In contrast to early efforts to accomplish b+g imaging with liquid xenon detectors [Gri07], scattering detectors as inserts into PET scanners [Yos20], or planar semiconductor detectors paired with scintillation cameras [Lan14], we propose instead to develop and demonstrate a single detector technology and associated data processing methods that can be used for both 511 keV annihilation photons and the higher-energy, singly- emitted gamma rays. Abbaszadeh (MPI) and Levin have pioneered an edge-on crossed-strip cadmium zinc telluride (CZT) detector approach to PET detectors that provides an ideal starting point [Abb16]. Among their attributes are high stopping power based on the edge-on geometry, 3D positioning that minimizes parallax, excellent energy resolution, and dynamic range up to 1.2 MeV in maximum photon energy deposited per interaction. Furthermore, when a photon undergoes an initial scatter followed by a photoelectric absorption, these modules yield data vectors that allow position and energy estimation for both interactions that can be analyzed with Compton kinematics [Abb17]. We will carry out a 2-year simulation and proof-of-principle phase (UG3) in which we demonstrate b+g detection with edge-on CZT modules and measure detector characteristics, develop simulations that support reconstructions, and demonstrate acquisitions with single and multiple isotopes. We will carry out a three-year UH3 phase to build a tomographic system with a field of view sufficient to investigate imaging of sophisticated dynamic phantoms and in vivo imaging of rodent brain, as a design study and precursor to a human brain system.
总结 我们提出了一种新的方法,混合成像模式,已被称为“B+g”或“pamma正电子” 成像[Gri 07],承诺同时克服1)单伽马射线光子的灵敏度限制 发射成像,2)区分两种不同正电子发射同位素的挑战,以及3) 在基于正电子探测的放射性同位素成像中固有的基于物理学的空间分辨率限制, 单光子湮没[Lan 14]。其目的是显着推进人类大脑的分子成像 通过允许更小的亚结构的可视化,更少量的放射性示踪剂摄取的量化,以及 先进多同位素研究中多种动态和空间摄取模式的同时测量 正常的大脑功能。使这一点可行所需的要素包括: 湮灭和伽马射线光子,可以产生丰富的数据,用于精确的能量,位置和时间估计 对于光电和康普顿相互作用,ii)在固件和软件中处理算法以进行分类, 最佳地利用可能发生的具有和不具有重合的信号的各种组合,iii) 基于可能性的重建算法,其结合了发射、检测、正电子 范围、非共线性和康普顿运动学,以及iv)衰减和对象的检测和补偿 运动-如果不加以解决,将成为分辨率和图像质量的限制因素。 与使用液体氙探测器实现B+g成像的早期努力相反[Gri 07], 探测器作为插入PET扫描仪[Yos 20],或与闪烁器配对的平面半导体探测器 相机[Lan 14],我们建议开发和演示单个探测器技术及其相关技术 数据处理方法,可用于511 keV湮灭光子和更高的能量,单- 放射出伽马射线。Abbaszadeh(MPI)和Levin开创了一种边缘交叉带镉锌 碲化物(CZT)探测器与PET探测器的方法提供了理想的起点[Abb 16]。他们的 属性是基于边缘几何形状的高阻止能力,最大限度地减少视差的3D定位, 出色的能量分辨率和动态范围高达1.2 MeV的最大光子能量沉积每 互动此外,当光子经历初始散射,随后是光电吸收时,这些光子将被吸收。 模块产生数据向量,允许对可以分析的两种相互作用进行位置和能量估计 康普顿运动学[Abb 17]。 我们将进行为期2年的模拟和原理验证阶段(UG 3),在此阶段,我们将演示B+g 检测与边缘上的CZT模块和测量探测器特性,开发模拟,支持 重建,并演示了单和多同位素的采集。我们将开展为期三年的 UH 3阶段,以建立一个断层扫描系统,其视场足以研究复杂的成像 啮齿动物脑的动态幻影和体内成像,作为设计研究和人脑系统的先驱。

项目成果

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Shiva Abbaszadeh其他文献

Shiva Abbaszadeh的其他文献

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{{ truncateString('Shiva Abbaszadeh', 18)}}的其他基金

A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium
基于工程非晶硫属化物合金的双层平板 X 射线探测器,用于量化冠状动脉钙
  • 批准号:
    10698174
  • 财政年份:
    2022
  • 资助金额:
    $ 43.22万
  • 项目类别:
A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium
基于工程非晶硫属化物合金的双层平板 X 射线探测器,用于量化冠状动脉钙
  • 批准号:
    10839539
  • 财政年份:
    2022
  • 资助金额:
    $ 43.22万
  • 项目类别:
A dual-layer flat panel x-ray detector based on an engineered amorphous chalcogenide alloy for quantifying coronary artery calcium
基于工程非晶硫属化物合金的双层平板 X 射线探测器,用于量化冠状动脉钙
  • 批准号:
    10504769
  • 财政年份:
    2022
  • 资助金额:
    $ 43.22万
  • 项目类别:
Empowering Diversity in X-ray Imaging: Training, Graduate Research, and Advancements in Detector Development
增强 X 射线成像的多样性:培训、研究生研究和探测器开发的进步
  • 批准号:
    10877539
  • 财政年份:
    2022
  • 资助金额:
    $ 43.22万
  • 项目类别:
High spatial resolution dedicated head and neck PET system based on cadmium zinc telluride detectors
基于碲化镉锌探测器的高空间分辨率专用头颈 PET 系统
  • 批准号:
    9789282
  • 财政年份:
    2018
  • 资助金额:
    $ 43.22万
  • 项目类别:
Enabling Brain Parametric Imaging of Alzheimer's disease with an Organ-dedicated PET
使用器官专用 PET 实现阿尔茨海默病的脑参数成像
  • 批准号:
    10286644
  • 财政年份:
    2018
  • 资助金额:
    $ 43.22万
  • 项目类别:
High spatial resolution dedicated head and neck PET system based on cadmium zinc telluride detectors
基于碲化镉锌探测器的高空间分辨率专用头颈 PET 系统
  • 批准号:
    10198923
  • 财政年份:
    2018
  • 资助金额:
    $ 43.22万
  • 项目类别:

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