Xue Hengzhi, Zhang Qiyang, Zou Sijuan, Zhang Weiguang, Zhou Chao, Tie Changjun, Wan Qian, Teng Yueyang, Li Yongchang, Liang Dong, Liu Xin, Yang Yongfeng, Zheng Hairong, Zhu Xiaohua, Hu Zhanli
College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Quant Imaging Med Surg. 2021 Feb;11(2):749-762. doi: 10.21037/qims-20-66.
Reducing the radiation tracer dose and scanning time during positron emission tomography (PET) imaging can reduce the cost of the tracer, reduce motion artifacts, and increase the efficiency of the scanner. However, the reconstructed images to be noisy. It is very important to reconstruct high-quality images with low-count (LC) data. Therefore, we propose a deep learning method called LCPR-Net, which is used for directly reconstructing full-count (FC) PET images from corresponding LC sinogram data.
Based on the framework of a generative adversarial network (GAN), we enforce a cyclic consistency constraint on the least-squares loss to establish a nonlinear end-to-end mapping process from LC sinograms to FC images. In this process, we merge a convolutional neural network (CNN) and a residual network for feature extraction and image reconstruction. In addition, the domain transform (DT) operation sends a priori information to the cycle-consistent GAN (CycleGAN) network, avoiding the need for a large amount of computational resources to learn this transformation.
The main advantages of this method are as follows. First, the network can use LC sinogram data as input to directly reconstruct an FC PET image. The reconstruction speed is faster than that provided by model-based iterative reconstruction. Second, reconstruction based on the CycleGAN framework improves the quality of the reconstructed image.
Compared with other state-of-the-art methods, the quantitative and qualitative evaluation results show that the proposed method is accurate and effective for FC PET image reconstruction.
在正电子发射断层扫描（PET）成像过程中降低辐射示踪剂剂量和扫描时间，可以降低示踪剂成本、减少运动伪影并提高扫描仪效率。然而，重建后的图像会有噪声。利用低计数（LC）数据重建高质量图像非常重要。因此，我们提出了一种名为LCPR-Net的深度学习方法，用于从相应的LC正弦图数据直接重建全计数（FC）PET图像。
基于生成对抗网络（GAN）框架，我们在最小二乘损失上施加循环一致性约束，以建立从LC正弦图到FC图像的非线性端到端映射过程。在此过程中，我们合并了卷积神经网络（CNN）和残差网络用于特征提取和图像重建。此外，域变换（DT）操作将先验信息发送到循环一致GAN（CycleGAN）网络，避免了学习这种变换需要大量计算资源的问题。
该方法的主要优点如下。首先，网络可以将LC正弦图数据作为输入直接重建FC PET图像。重建速度比基于模型的迭代重建更快。其次，基于CycleGAN框架的重建提高了重建图像的质量。
与其他现有方法相比，定量和定性评估结果表明，所提出的方法对于FC PET图像重建是准确有效的。
