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.]

项目概要 具有出色重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) 有望显着提高有效 511 keV 光子灵敏度的能力。 沿系统响应线限制事件数据定位湮灭起源，提供改进的信号到 通过将 511 keV 光子更接近其真实情况关联起来，提高噪声比 (SNR) 和重建图像质量 随着点击率 (CTR) 的提高，信噪比 (SNR) 也会随之提高，这也是 PET 仪器的主要目标。 研发的目的是推动系统 CTR ≤100 ps 半高全宽 (FWHM) 这个水平。 性能，事件被限制在≤1.5 cm，相对于具有 没有 TOF 功能，能够 ≤100 ps FWHM CTR 的先进系统实际上会增加一倍以上或 与最先进的临床 TOF-PET 系统相比，有效 511 keV 系统灵敏度提高了四倍 （250-400 ps FWHM CTR）因此，提高 CTR 也是大大提高系统灵敏度的途径，而无需。 增加检测量和系统材料成本 由分段阵列组成的标准 PET 检测器。 高纵横比闪烁晶体元件和激进的电子信号复用无法实现这一点 性能水平，并最终受到光收集效率差、深度相关闪烁的限制 光电探测器看到的光子渡越时间抖动，以及最佳鉴别器时间拾取的电子信噪比较差 和 511 keV 光子相互作用时间估计 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，允许闪烁光子到达每个光电传感器像素 可以对单片探测器中闪烁光子的时空到达时间进行计数并直接数字化。 本质上携带 511 keV 光子能量、三维 (3D) 位置和时间的所有信息 相互作用，以及相互作用相关的闪烁光子传输偏斜的 3D 位置 [因此，这种新探测器。 读出概念能够直接数字化耦合的光电传感器阵列上的时间闪烁光图 单片闪烁体为机器学习 (ML) 技术提取 3D 提供了独特的机会 大面积、厚的相互作用估计器的定位和时间（511 keV 光子探测效率高） 我们将利用这一新的进步来实现性能统计极限的检测器模块。 研究应用于来自原型探测器模块的数字化光子数据流的机器学习性能 在设计中展示高分辨率、三维定位功能和点击率 所提出的 PET 检测器技术可以对检测效率产生重大影响。 有效灵敏度的显着提升可实现 PET 成像的图像信噪比。 用于大幅减少示踪剂剂量并缩短扫描时间/增加患者吞吐量，或更好地 在存在显着背景的情况下可视化和量化较小的病变/特征，这很重要 这些功能可以使 PET 更加实用和准确，并有助于扩大其在患者中的作用 管理。]