AI 中文总结
研究针对 LAFOV PET 扫描仪背景率高的问题，将符合分类设为多类问题，用蒙特卡洛模拟评估多种分类器作重建前滤波器，对比两个特征集，发现 4 特征模型泛化性好，最佳模型优于传统方法，但提高图像质量还需改进。
AI 中文摘要
LAFOV PET 扫描仪的引入提高了灵敏度，但也增加了来自偶然符合、体模散射和探测器散射光子的背景率。机器学习方法虽已用于 PET 成像的背景减少，但多针对后处理中的特定背景成分，而非对原始数据进行逐事件分类。本文将符合分类表述为有监督多类问题，用西门子 Biograph Vision Quadra 扫描仪的蒙特卡洛模拟及 NEMA IEC 和拟人化 XCAT 体模评估 XGBoost、AdaBoost 和神经网络分类器作为重建前滤波器。研究了两个特征集，通过跨体模推理的系统稳健性研究表明，4 特征模型在不同体模几何形状上的泛化性明显更好。最佳模型在 NEMA IEC 和 XCAT 体模上的准确率分别高达 0.74 和 0.69，优于传统基于几何的切割方法。然而，该紧凑特征集对体模内散射符合的抑制有限，且可能导致非平凡空间模式。鉴于散射符合是临床条件下的主要背景成分，这表明该方法虽可有效替代传统基于切割的选择且与几何无关，但要进一步提高图像质量，还需更大的输入表示或对体模散射成分进行专门处理。
英文摘要
The introduction of LAFOV PET scanners brings significant sensitivity gains but also a substantial increase in the background rate from accidental coincidences, phantom-scattered and detector-scattered photons. While machine learning methods have been applied to background reduction in PET imaging, they target specific background components in post-processing rather than event-by-event classification on the raw data. In this work, we formulate coincidence classification as a supervised multi-class problem and evaluate XGBoost, AdaBoost and Neural Network classifiers as pre-reconstruction filters, using Monte Carlo simulations of the Siemens Biograph Vision Quadra scanner with NEMA IEC and anthropomorphic XCAT phantoms. We investigate two feature sets: a 4-feature representation based on the Attenuation Factor, photon time difference, energy sum, and energy difference, and an extended 6-feature set that incorporates topology-based variables. A systematic robustness study via cross-phantom inference reveals that the 4-feature models generalise significantly better across different phantom geometries, with XGBoost suffering an accuracy loss of only 0.04 compared to 0.13 for the 6-feature variant. Our best models achieve accuracies of up to 0.74 and 0.69 for the NEMA IEC and XCAT phantoms, respectively, outperforming traditional geometry-based cuts. However, we show that this compact feature set not only provides limited suppression of in-phantom scattered coincidences, but it also can lead to non-trivial spatial patterns. With scattered coincidences being the dominant background component in clinical conditions, this suggests that while the method serves as an effective and geometry-agnostic replacement for traditional cut-based selection, meaningful further gains in image quality will require either larger input representations or dedicated treatment of the phantom-scattered component.
