用脉冲神经网络加速正电子发射断层扫描中的光子符合检测
PETNet -- Coincident Particle Event Detection using Spiking Neural Networks
- 将探测器事件视为脉冲序列，通过监督学习识别光子符合对
- 最高准确率达95.2%，比传统算法快36倍
- 适合需要低功耗高速处理的粒子物理与医学成像场景
脉冲神经网络（SNN）因其生物合理性与低功耗特性，被视为传统人工神经网络的替代方案。其时变特性特别适用于处理时间分辨、稀疏二值数据。本文研究SNN在正电子发射断层扫描（PET）数据中检测光子符合的潜力。PET通过注射放射性示踪剂并探测发射光子来成像。关键后处理任务是过滤因吸收或散射导致的无效事件。我们提出的PETNet将探测器击中视为二值脉冲序列，以监督方式学习识别光子符合对。引入专用多目标损失函数，并在两种应用场景下验证了显式建模探测器几何结构的效果。结果表明，PETNet在模拟数据上可达到95.2%的最大符合检测F1值，同时比传统方法快36倍，凸显了SNN在粒子物理应用中的巨大潜力。
原文摘要 · Abstract (English)
Spiking neural networks (SNN) hold the promise of being a more biologically plausible, low-energy alternative to conventional artificial neural networks. Their time-variant nature makes them particularly suitable for processing time-resolved, sparse binary data. In this paper, we investigate the potential of leveraging SNNs for the detection of photon coincidences in positron emission tomography (PET) data. PET is a medical imaging technique based on injecting a patient with a radioactive tracer and detecting the emitted photons. One central post-processing task for inferring an image of the tracer distribution is the filtering of invalid hits occurring due to e.g. absorption or scattering processes. Our approach, coined PETNet, interprets the detector hits as a binary-valued spike train and learns to identify photon coincidence pairs in a supervised manner. We introduce a dedicated multi-objective loss function and demonstrate the effects of explicitly modeling the detector geometry on simulation data for two use-cases. Our results show that PETNet can outperform the state-of-the-art classical algorithm with a maximal coincidence detection $F_1$ of 95.2%. At the same time, PETNet is able to predict photon coincidences up to 36 times faster than the classical approach, highlighting the great potential of SNNs in particle physics applications.
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