1
|
Gong Y, Shan H, Teng Y, Tu N, Li M, Liang G, Wang G, Wang S. Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising. IEEE Trans Radiat Plasma Med Sci 2021; 5:213-223. [PMID: 35402757 PMCID: PMC8993163 DOI: 10.1109/trpms.2020.3025071] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Due to the widespread use of positron emission tomography (PET) in clinical practice, the potential risk of PET-associated radiation dose to patients needs to be minimized. However, with the reduction in the radiation dose, the resultant images may suffer from noise and artifacts that compromise diagnostic performance. In this paper, we propose a parameter-transferred Wasserstein generative adversarial network (PT-WGAN) for low-dose PET image denoising. The contributions of this paper are twofold: i) a PT-WGAN framework is designed to denoise low-dose PET images without compromising structural details, and ii) a task-specific initialization based on transfer learning is developed to train PT-WGAN using trainable parameters transferred from a pretrained model, which significantly improves the training efficiency of PT-WGAN. The experimental results on clinical data show that the proposed network can suppress image noise more effectively while preserving better image fidelity than recently published state-of-the-art methods. We make our code available at https://github.com/90n9-yu/PT-WGAN.
Collapse
Affiliation(s)
- Yu Gong
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, and Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| | - Hongming Shan
- Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China, and the Shanghai Center for Brain Science and Brain-Inspired Technology, Shanghai 201210, China
| | - Yueyang Teng
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China, and the Key Laboratory of Intelligent Computing in Medical Images, Ministry of Education, Shenyang 110169, China
| | - Ning Tu
- PET-CT/MRI Center and Molecular Imaging Center, Wuhan University Renmin Hospital, Wuhan, 430060, China
| | - Ming Li
- Neusoft Medical Systems Co., Ltd, Shenyang 110167, China
| | - Guodong Liang
- Neusoft Medical Systems Co., Ltd, Shenyang 110167, China
| | - Ge Wang
- Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA
| | - Shanshan Wang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China
| |
Collapse
|