论文标题
Petalo读数系统中康普顿事件的处理
Processing of Compton events in the PETALO readout system
论文作者
论文摘要
petalo（基于液体氙气的正电子发射TOF tof仪）利用了液体氙气作为用于宠物探测器的闪烁体的独特特征。在这里，详细介绍了最初的仿真研究，该研究突出了这种检测器的潜力，并概述了511 Kev Gamma射线重建和完整的PET图像重建中所采取的步骤。特别是，为了标记由于康普顿散射而导致的不良重建的伽玛射线，采用了基于神经网络的方法。已经发现，尽管大量事件会经历康普顿散射，但并非所有这些事件都一定会重建。对于确定基于神经网络的事件标签的在线实施是否会提供足够的图像质量恢复，需要进一步研究。
PETALO (Positron Emission TOF Apparatus based on Liquid xenOn) exploits the unique characteristics of liquid xenon as a scintillator for use in a PET detector. Here initial simulation studies are detailed which highlight the potential of such a detector and outline the steps taken in the reconstruction of 511 keV gamma rays and in the full PET image reconstruction. In particular, a neural network-based approach is conceived in order to tag gamma rays that are poorly reconstructed due to Compton scattering. It is found that though a significant fraction of events undergo Compton scattering, not all of these events will necessarily be poorly reconstructed. Further study is necessary to determine whether or not an online implementation of the neural network-based event tagging will provide sufficient recovery of the image quality.
