AI 中文总结
研究基于光子计数计算机断层扫描数据进行神经网络材料识别的可行性，以感兴趣区域光谱向量为输入，在12个探测器阈值设置下获取原始断层切片，经插值等处理，考虑不同数据集并切片级划分数据用于模型训练。
AI 中文摘要
本工作研究基于光子计数计算机断层扫描（PCCT）数据进行神经网络材料识别的可行性。输入数据是从包含镧、钕、钆基样本以及空气、水、骨和聚甲基丙烯酸甲酯（PMMA）的体模重建断层切片中的感兴趣区域（ROIs）提取的光谱向量。原始断层切片在12个探测器阈值设置下获取。模型训练时，光谱以一个阈值单位的步长从阈值45插值到阈值165的均匀网格上。考虑了大ROIs、小ROIs及其组合数据集。为避免从同一断层切片提取的相关ROIs导致性能高估，数据在切片级别被分为训练、验证和测试子集。
英文摘要
This work investigates the feasibility of neural-network-based material identification based on photon-counting computed tomography (PCCT) data. The input data were spectral vectors extracted from regions of interest (ROIs) in reconstructed tomographic slices of a phantom containing La-, Nd-, and Gd-based samples, as well as air, water, bone, and polymethyl methacrylate (PMMA). The original tomographic slices were acquired at 12 detector threshold settings (THL = 45, 55, 65, 75, 85, 95, 105, 115, 125, 135, 145, 165). For model training, the spectra were interpolated onto a uniform grid from THL 45 to THL 165 with a step of one THL unit. Large ROIs, small ROIs, and their combined dataset were considered. To avoid an overestimated performance caused by correlated ROIs extracted from the same tomographic slice, the data were split into training, validation, and test subsets at the slice level.
