Photon counting radiation detectors have become an integral part of medical imaging modalities such as Positron Emission Tomography or Computed Tomography. One of the most promising detectors is the wide bandgap room temperature semiconductor detectors, which depends on the interaction gamma/x-ray photons with the detector material involves Compton scattering which leads to multiple interaction photon events (MIPEs) of a single photon. For semiconductor detectors like CdZnTeSe (CZTS), which have a high overlap of detected energies between Compton and photoelectric events, it is nearly impossible to distinguish between Compton scattered events from photoelectric events using conventional readout electronics or signal processing algorithms. Herein, we report a deep learning classifier CoPhNet that distinguishes between Compton scattering and photoelectric interactions of gamma/x-ray photons with CdZnTeSe (CZTS) semiconductor detectors. Our CoPhNet model was trained using simulated data to resemble actual CZTS detector pulses and validated using both simulated and experimental data. These results demonstrated that our CoPhNet model can achieve high classification accuracy over the simulated test set. It also holds its performance robustness under operating parameter shifts such as Signal-Noise-Ratio (SNR) and incident energy. Our work thus laid solid foundation for developing next-generation high energy gamma-rays detectors for better biomedical imaging.

翻译：光子计数辐射探测器已成为正电子发射断层扫描或计算机断层扫描等医学成像模式不可或缺的组成部分。最具前景的探测器之一是基于宽禁带室温半导体探测器，其工作机制涉及伽马/X射线光子与探测器材料相互作用，包含康普顿散射，这导致单个光子产生多次相互作用光子事件（MIPEs）。对于CdZnTeSe（CZTS）等半导体探测器，其康普顿事件与光电事件检测到的能量高度重叠，使得利用传统读出电子学或信号处理算法几乎无法区分两者。本文报道了一种深度学习分类器CoPhNet，可区分伽马/X射线光子与CdZnTeSe（CZTS）半导体探测器相互作用的康普顿散射与光电效应。我们的CoPhNet模型使用模拟数据训练以模拟实际CZTS探测器脉冲，并通过模拟和实验数据进行了验证。结果表明，CoPhNet模型在模拟测试集上实现了高分类准确率，并且在信噪比（SNR）和入射能量等操作参数偏移下仍保持性能稳健性。因此，本研究为开发用于更好生物医学成像的新一代高能伽马射线探测器奠定了坚实基础。