In this study, a method based on a deep stacking network is proposed to solve the signal separation problem of dynamic dual-tracer PET. The advantage of this method is that it avoids requirements for prior information of tracers, and a staggered injection. The proposed model is pre-trained with restricted Boltzmann machines and fine-tuned in a manner, which the output of the last training epoch was used as additional input in the current epoch to update the model parameters. We train the network to learn the complex relationship between dual-tracer time-activity curves and separated single tracer data using a mean square error objective function. Monte Carlo simulations are employed to test the accuracy and robustness of the proposed method on the total counts and reconstruction algorithm. Quantification results show that the proposed method outperforms the existing approach. Experiments with real data further validate previous results on synthetic data.

本研究提出了一种基于深度堆叠网络的方法来解决动态双示踪PET的信号分离问题。这种方法的优点是它避免了对先验的要求示踪剂信息和交错注射。所提出的模型使用受限玻尔兹曼机进行预训练并以某种方式进行微调，其中最后一个训练时期的输出用作当前时期的附加输入以更新模型参数。我们训练网络使用均方误差目标函数来学习双示踪时间活动曲线和分离的单示踪数据之间的复杂关系。蒙特卡罗模拟被用来测试所提出的方法对总计数和重建算法的准确性和鲁棒性。量化结果表明，所提出的方法优于现有方法。使用真实数据进行的实验进一步验证了之前在合成数据上的结果。