AI-accelerated optical simulation for fast timing nuclear imaging

项目摘要/摘要 提高高空间分辨率的量化正在推动核成像技术的发展。 正电子发射断层扫描仪和单光子发射计算机断层扫描 使用辐射探测器，当成像过程为 明白了。优化光学机制，如闪烁或在核心的即时光子发射 这些探测器对推动技术进步至关重要，也是本提案的重点。由于它的复杂性 对于这些现象和难以通过实验分离其组成部分的问题，辐射研究 探测器光学依赖于整合高能物理和低能物理的模拟。目前没有模拟器提供 理解从探测器到系统的图像形成所需的速度和保真度。 我们建议开发一种完全不同的基于人工智能的高保真光学建模框架， 允许在系统处快速生成、收集和处理多维光学信息 水平。通过用深度学习方法取代单个光子跟踪，我们预计将加快 在涉及广泛的光学光子跟踪的系统中的几个数量级的模拟，例如 大型探测器或快速定时探测器。我们将这份R01提案组织为三个具体目标，重点是 在Geant4/GATE模拟器中实现该框架并将其应用于飞行时间(TOF) 宠物。GATE是一个处于核医学模拟前沿的免费开源平台。我们有一条赛道 开发光学建模策略的记录，并创建了LUT Davis模型。这笔赠款将设计和 实施Optigan，这是一个将经过高保真训练的自定义生成性对抗网络(GAN) 基于LUT Davis模型的仿真。新的光传输功能和晶体-光电探测器接口 混合粒子光学和波动光学的模型将被开发并集成到Optigan中(目标1和2)。 我们已经广泛地研究和开发了切伦科夫基辐射探测器，这是提示之一 最常用的光子发射机制是实现低于50ps的时间分辨率和解锁 无需重建的PET。要发展基于光子的快速PET系统，必须解决几个问题： 如何用新材料改善这些瞬发光子的产生和传输，如何提高 它们的收集，以及如何利用即时的光子信息来实现快速符合计时。这些 问题推动了TOF PET的创新探测器光学和算法的发展，我们将 与Optigan一起进行调查，并进行实验和理论工作(目标2和3)。 这笔赠款的目标是通过史无前例的模拟实现探测器技术的飞跃 在核成像扫描仪中利用快速探测器的能力和新战略。显影探测器 现在的技术使下一代扫描仪能够响应临床和研究需求 核医学是必不可少的，因为整合这些进展需要数年时间才能商业化。

Project Summary/ Abstract Improving quantification at high spatial resolution is driving technology developments in nuclear imaging. Positron emission tomography (PET) scanners and single photon emission computed tomography (SPECT) use radiation detectors, which performance can be improved when the image formation process is understood. Optimizing optical mechanisms such as scintillation or prompt photon emission at the core of these detectors is essential to advance the technology and is the focus of this proposal. Due to the complexity of these phenomena and the difficulty to disentangle their components experimentally, research on radiation detector optics relies on simulations integrating high and low energy physics. No simulators currently offer the speed and fidelity necessary to understand image formation from the detector to the system. We propose to develop a radically different AI-based high-fidelity optical modeling framework, allowing multidimensional optical information to be rapidly generated, collected, and processed at the system level. By replacing individual photon tracking with a deep-learning approach, we expect to accelerate simulations by several orders of magnitude in systems involving extensive optical photon tracking, such as large detectors or fast timing detectors. We organize this R01 proposal in three specific aims focusing on implementing this framework in the Geant4/GATE simulators and applying it to time-of-flight (TOF) PET. GATE is a free opensource platform at the forefront of nuclear medicine simulation. We have a track record of developing optical modeling strategies and created the LUT Davis model. This grant will design and implement the optiGAN, a custom generative adversarial network (GAN) that will be trained with high-fidelity simulations based on the LUT Davis model. New light transport features and crystal-photodetector interface models mixing particle and wave optics will be developed and integrated into the optiGAN (Aims 1 and 2). We have extensively studied and developed Cerenkov-based radiation detectors, one of the prompt photon emission mechanisms most pursued to achieve timing resolution below 50 ps and unlock reconstruction-free PET. To develop prompt photon-based PET systems several questions must be solved: how to improve the production and transport of these prompt photons with new materials, how to improve their collection, and how to harness the prompt photon information for fast coincidence timing. These questions motivate the development of innovative detector optics and algorithms for TOF PET, which we will investigate with the optiGAN together with experimental and theoretical work (Aims 2 and 3). The objective of this grant is to enable a leap in detector technology through unprecedented simulation capabilities and new strategies to leverage fast detectors in nuclear imaging scanners. Developing detector technology now that enables the next generation of scanners to respond to clinical and research needs of nuclear medicine is essential, as the integration of these advances requires years before commercialization.