A TOF, DOI, MRI compatible PET detector to support sub-millimeter neuroPET imaging

兼容 TOF、DOI、MRI 的 PET 探测器，支持亚毫米神经 PET 成像

• 批准号: 9791188
• 负责人: Sergei Dolinsky
• 金额: $ 53.7万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-25 至 2021-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9791188
• 关键词: Aging; Algorithms; Area; Benchmarking; Brain; Brain imaging; Clinical; Crystallization; Data; Detection; Development; Devices; Dimensions; Disease; Electronics; Elements; Evaluation; Event; Functional Imaging; Geometry; Goals; Guidelines; Human; Image; Imaging technology; Individual; Investigation; Laboratory Research; Lead; Length; Machine Learning; Magnetic Resonance Imaging; Mathematics; Measurement; Measures; Methods; Modeling; Monte Carlo Method; Multimodal Imaging; Neurosciences; Optics; Outcome; Performance; Physiologic pulse; Positioning Attribute; Positron-Emission Tomography; Property; Research; Research Personnel; Resolution; Role; Running; Side; Silicon; Surface; System; Technology; Testing; Time; United States National Institutes of Health; Universities; Vendor; Washington; Work; base; brain research; design; detector; digital; imaging detector; imaging system; improved; innovative neurotechnologies; innovative technologies; learning strategy; neuroimaging; next generation; novel; operation; prototype; temporal measurement; tool

Abstract The overall goal of this research is to develop next generation positron emission tomography (PET) detector technology to support non-invasive, quantitative brain imaging at spatial and temporal resolutions currently not achievable with human neuro-PET systems. The developed PET detector technology will also be compatible with operation in an MRI system. The proposed research is targeted to the NIH Brain Research through Advancing Innovative Neurotechnologies (BRAIN) initiative. Human brain imaging with PET/MRI will be an essential tool in neuroscience studies to "Develop innovative technologies to understand the human brain and treat its disorders; create and support integrated brain research networks." The key advancement that we introduce is a PET detector with <100 psec time-of-flight (TOF) PET coincidence resolution, <2 mm continuous depth of interaction (DOI) positioning and intrinsic detector spatial resolution to support <1 mm PET image resolution throughout the system imaging field of view (FOV). Detector modules have been designed that individually achieve these performance metrics; however, no detector module has been designed that supports all of them. To make disruptive advancements in neuro-imaging using PET, one must push the image spatial resolution (i.e., currently 2-3 mm image resolution) as well as coincidence timing resolution and also be MRI compatible. The impact of this project is that we will advance the state of the art in all of these critical performance areas. We will achieve these goals by first understanding the role that different PET detector performance parameters have on task-based figures of merit for neuroPET imaging. We will investigate how both TOF and image resolution impact figure of merit performance for estimation, detection and characterization imaging tasks. Monte Carlo simulation will be used along with both object-based (i.e., mathematical) and anthropomorphic digital phantoms. Next we will optimize SiPM device selection, electronics and detector geometry for <100 psec TOF coincidence timing, 1 mm intrinsic spatial resolution and <2 mm DOI positioning resolution. We will build and characterize a prototype PET detector module utilizing a novel dual- sided slat crystal detector design. To advance coincidence timing performance we will investigate the use of machine learning to estimate the arrival time of detected events. Finally, we will optimize the detector design for MRI compatibility. We will fully test and characterize performance of our prototype detector on the benchtop and in a MRI scanner while running clinical MRI pulse sequences. At the end of this developmental project we will be in position to build a state of the art, MRI compatible, TOF, DOI PET imaging system.

抽象的 这项研究的总体目标是开发下一代正电子发射断层扫描（PET）检测器 在空间和时间分辨率上支持非侵入性，定量大脑成像的技术目前尚未 可以通过人类神经-PET系统实现。开发的宠物探测器技术也将兼容 在MRI系统中操作。拟议的研究针对NIH大脑研究 推进创新的神经技术（大脑）倡议。用宠物/MRI成像的人脑成像将是 神经科学研究的基本工具，以“开发创新技术来了解人脑和 治疗其疾病；建立和支持综合的大脑研究网络。”我们的关键进步 介绍是一个宠物探测器，飞行时间<100（TOF）宠物巧合分辨率，<2 mm连续 相互作用的深度（DOI）定位和内在检测器空间分辨率支持<1 mm PET图像 整个系统成像视图（FOV）的分辨率。探测器模块已经设计了 单独实现这些性能指标；但是，尚未设计任何探测器模块来支持 所有人。要使用PET在神经形象中取得破坏性的进步，必须推动图像空间 分辨率（即当前2-3 mm图像分辨率）以及巧合的正时分辨率，也是MRI 兼容的。该项目的影响是，我们将在所有这些关键 性能领域。我们将首先了解不同宠物探测器的作用来实现这些目标 性能参数具有基于任务的神经成像功能的功绩。我们将调查如何 TOF和图像分辨率的效果绩效的影响图，用于估计，检测和 表征成像任务。蒙特卡洛模拟将与两个基于对象的模拟一起使用（即 数学）和拟人化数字幻影。接下来，我们将优化SIPM设备选择，电子设备 <100 psec TOF重合时机，1 mm内在空间分辨率和<2 mm doi的探测器几何形状 定位分辨率。我们将利用新颖的双重培养宠物探测器模块来构建和表征原型的宠物探测器模块 侧面SLAT晶体检测器设计。为了提高巧合的时机性能，我们将调查使用 机器学习以估计被检测到的事件的到达时间。最后，我们将优化检测器设计 用于MRI兼容性。我们将完全测试和表征台式上原型检测器的性能 在运行临床MRI脉冲序列时，在MRI扫描仪中。在这个发展项目的结尾，我们 将可以建立最先进的状态，MRI兼容，TOF，DOI PET成像系统。