Machine Learning with Scintillation Photon Counting Detectors to Advance PET Imaging Performance

利用闪烁光子计数探测器进行机器学习以提高 PET 成像性能

• 批准号: 10742435
• 负责人: Joshua William Cates
• 金额: $ 50.28万
• 依托单位: UNIVERSITY OF CALIF-LAWRENC BERKELEY LAB
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-26 至 2025-09-24
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10742435
• 关键词: 3-Dimensional; Address; Algorithms; Area; Clinical; Collection; Coupled; Data; Deposition; Detection; Dose; Electronics; Elements; Event; Goals; Image; Image Enhancement; LSO crystal; Lesion; Light; Machine Learning; Maps; Measurement; Modeling; Noise; Optics; Pathway interactions; Patients; Performance; Photons; Physics; Positioning Attribute; Positron-Emission Tomography; Process; Radiation Dose Unit; Resolution; Role; Scanning; Scheme; Scintillation Counting; Signal Transduction; Stream; System; Techniques; Technology; Temperature; Thick; Time; Tracer; Training; Variant; Visualization; Width; advanced system; attenuation; convolutional neural network; cost; data streams; deep learning; density; design; detector; hands-on learning; image reconstruction; imaging study; improved; instrumentation; machine learning algorithm; models and simulation; neural network; novel; operation; photon-counting detector; prototype; research and development; response; signal processing; simulation; spatiotemporal; statistics; two-dimensional; uptake; 3-Dimensional; Address; Algorithms; Area; Clinical; Collection; Coupled; Data; Deposition; Detection; Dose; Electronics; Elements; Event; Goals; Image; Image Enhancement; LSO crystal; Lesion; Light; Machine Learning; Maps; Measurement; Modeling; Noise; Optics; Pathway interactions; Patients; Performance; Photons; Physics; Positioning Attribute; Positron-Emission Tomography; Process; Radiation Dose Unit; Resolution; Role; Scanning; Scheme; Scintillation Counting; Signal Transduction; Stream; System; Techniques; Technology; Temperature; Thick; Time; Tracer; Training; Variant; Visualization; Width; advanced system; attenuation; convolutional neural network; cost; data streams; deep learning; density; design; detector; hands-on learning; image reconstruction; imaging study; improved; instrumentation; machine learning algorithm; models and simulation; neural network; novel; operation; photon-counting detector; prototype; research and development; response; signal processing; simulation; spatiotemporal; statistics; two-dimensional; uptake

Project Summary Clinical time-of-flight positron emission tomography (TOF-PET) systems capable of excellent coincidence time resolution (CTR) promise to drastically enhance effective 511 keV photon sensitivity. The ability to more precisely localize annihilation origins along system response lines constrains event data, providing improved signal-to- noise ratio (SNR) and reconstructed image quality by associating 511 keV photons more closely to their true origin. This SNR enhancement increases as CTR is improved, and a major goal of ongoing PET instrumentation research and development is to push system CTR ≤100 ps full-width-at-half-maximum (FWHM). At this level of performance, events are constrained ≤1.5 cm, providing a ≥five-fold increase in SNR relative to a system with no TOF capability. Advanced systems capable of ≤100 ps FWHM CTR would effectively more than double or quadruple the effective 511 keV system sensitivity, in comparison to state-of-the-art, clinical TOF-PET systems (250-400 ps FWHM CTR). Thus, advancing CTR is also a pathway for greatly improved system sensitivity without increasing detection volume and system material cost. Standard PET detectors comprising segmented arrays of high-aspect-ratio scintillation crystal elements and aggressive electronic signal multiplexing cannot achieve this level of performance and are ultimately limited by poor light collection efficiency, depth-dependent scintillation photon transit time jitter seen by the photodetector, and poor electronic SNR for optimal discriminator time pickoff and 511 keV photon time of interaction estimation. To address this, we are developing a new detector readout concept for monolithic scintillation detectors which allows scintillation photons arriving at each photosensor pixel to be counted and directly digitized. The spatiotemporal arrival time of scintillation photons in monolithic detectors intrinsically carries all information on 511 keV photon energy, three-dimensional (3D) position and time of interaction, and 3D position of interaction dependent scintillation photon transit skew. [Thus, this new detector readout concept’s ability to directly digitize the temporal scintillation light maps on photosensor arrays coupled to monolithic scintillators offers a unique opportunity for machine learning (ML) techniques to extract 3D positioning and time of interaction estimators in large area, thick (high 511 keV photon detection efficiency) detector modules that are at the statistical limit of performance. We will leverage this new advancement to investigate the performance of ML applied to the digitized photon data streams from a prototype detector module to demonstrate high resolution, three-dimensional positioning capabilities and CTR in a design that also makes no sacrifices on detection efficiency. The proposed PET detector technology can have a significant impact on quantitative PET imaging. The image SNR enabled by the significant boost in effective sensitivity can be employed to substantially reduce tracer dose and shorten scan time/increase patient throughput, or to better visualize and quantify smaller lesions/features in the presence of significant background, which are important features that can make PET more practical and accurate, as well as help to expand its roles in patient management.]

项目摘要 临床飞行时间正电子发射断层扫描（TOF-PET）系统，具有出色的巧合时间 分辨率（CTR）有望大大增强有效的511 KEV光子灵敏度。更精确的能力 沿系统响应线局部化歼灭起源会限制事件数据，从而改善了信号到信号。 噪声比（SNR）和重建图像质量通过将511 kev照片与它们的真实相关联。 起源。随着CTR的改善，SNR增强的增强和正在进行的宠物仪器的主要目标是 研发是为了推动系统CTR≤100PS全宽度最大宽度（FWHM）。在这个级别 性能，事件的约束≤1.5厘米，相对于系统，SNR的增加了≥five倍倍 没有TOF功能。能够≤100ps fwhm ctr的高级系统有效地超过两倍或 与最先进的临床TOF-PET系统相比 （250-400 PS FWHM CTR）。那就前进的CTR也是大大提高系统灵敏度的途径 增加检测量和系统材料成本。标准宠物探测器完成分段阵列的 高光谱比率闪烁晶体元素和侵略性电子信号多路复用无法实现 性能水平，最终受光收集效率，深度依赖性闪烁的限制 光电探测器看到光子传输时间抖动，而电子snr差，可用于最佳鉴别器时间选择 和511相互作用估计的KEV光子时间。为了解决这个问题，我们正在开发新的检测器读数 整体闪烁探测器的概念，该检测器允许闪烁的照片到达每个光电传感器像素 要计算并直接数字化。整体探测器中闪烁照片的空间临时到达时间 本质上包含有关511 KEV光子能量，三维（3D）位置和时间的所有信息 相互作用和相互作用依赖性闪烁光子传输偏斜的3D位置。 [因此，这个新检测器 读数概念的能力直接数字化光电传感器阵列上的临时闪光灯图 对于单片闪烁体，为机器学习（ML）提取3D提供了独特的机会（ML） 大面积（高511 KEV光子检测效率）的位置和相互作用估计器的时间 具有性能统计限制的检测器模块。我们将利用这一新的进步 研究从原型检测器模块应用于数字化光子数据流的ML的性能 为了展示高分辨率，三维定位功能和CTR的设计 没有关于检测效率的牺牲。拟议的宠物探测器技术可以对 定量宠物成像。通过有效敏感性的显着提升可以启用图像SNR 用于大大减少示踪剂剂量并缩短扫描时间/增加患者吞吐量，或者更好 在存在重要背景的情况下可视化和量化较小的病变/特征，这很重要 可以使宠物更实用和准确的功能，并有助于扩大其在患者中的角色 管理。]