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

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 具有极佳符合时间的临床飞行时间正电子发射断层扫描(TOF-PET)系统 分辨率(CTR)承诺大幅提高有效的511keV光子灵敏度。能够更准确地 沿系统响应线局部化湮灭源约束事件数据，提供改进的信号到 噪声比(SNR)和重建图像质量，使511keV光子更接近其真实状态 起源。这种SNR增强随着CTR的提高而增加，这也是当前PET仪器的一个主要目标 研发的是推系统CTR≤100ps的半高全宽(FWHM)。在这个层面上 性能方面，事件限制为≤1.5 cm，与以下系统相比，≥的信噪比提高了五倍 没有飞行时间能力。支持≤100ps半高宽CTR的先进系统将有效地将 与最先进的临床TOF-PET系统相比，有效的511keV系统的灵敏度提高了四倍 (250-400 ps半高宽CTR)。因此，推进CTR也是一条大大提高系统灵敏度的途径 增加了检测量和系统材料成本。由分段阵列组成的标准PET探测器 高宽高比闪烁晶体元素和积极的电信号多路传输无法实现这一点 性能水平，并最终受限于较差的光收集效率、依赖深度的闪烁 光电探测器看到的光子传输时间抖动，以及用于最佳鉴别器时间拾取的较差的电子SNR 511keV光子相互作用时间估算。为了解决这个问题，我们正在开发一种新的探测器读数 单片闪烁探测器的概念，它允许闪烁光子到达每个光电传感器像素 被计数并直接数字化。单片探测器中闪烁光子的时空到达时间 本质上携带关于511keV光子能量、三维(3D)位置和时间的所有信息 相互作用，以及依赖于相互作用的闪烁光子输运偏斜的3D位置。[因此，这种新的探测器 Readout Concept直接数字化耦合光电传感器阵列上的时间闪烁光图的能力 单片闪烁体为机器学习(ML)技术提供了一个独特的机会来提取3D 大面积、厚(高511keV光子探测效率)相互作用估计器的定位和时间 处于统计性能极限的探测器模块。我们将利用这一新的进步来 研究了ML应用于原型探测器模块的数字化光子数据流的性能 在设计中展示高分辨率、三维定位能力和CTR 在检测效率上没有任何牺牲。建议的PET探测器技术可以对 定量PET成像。通过有效灵敏度的显著提高而实现的图像SNR可以 用于显著减少示踪剂剂量和缩短扫描时间/增加患者吞吐量，或更好地 在有重要背景的情况下可视化和量化较小的病变/特征，这是重要的 可使PET更加实用和准确，并有助于扩大其在患者中的作用的功能 管理。]