LCPR-Net: low-count PET image reconstruction using the domain transform and cycle-consistent generative adversarial networks

Quantitative Imaging in Medicine and Surgery

article

Hengzhi Xue¹  Dong Liang²  Xin Liu²  Yongfeng Yang²  Hairong Zheng²  Xiaohua Zhu³  Zhanli Hu²  Qiyang Zhang²  Sijuan Zou⁵  Weiguang Zhang³  Chao Zhou³  Changjun Tie²  Qian Wan²  Yueyang Teng¹  Yongchang Li² 

[1] College of Medicine and Biological Information Engineering, Northeastern University;Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences;Department of Nuclear Medicine, Sun Yat-sen University Cancer Center;Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences;Department of Nuclear Medicine and PET, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology

关键词: Positron emission tomography (PET);    image reconstruction;    deep learning;    adversarial learning;   

DOI  :  10.21037/qims-20-66

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: 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. Methods: 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. Results: 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. Conclusions: 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.

【 授权许可】

All Rights reserved