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Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising by Gong, Yu; Shan, Hongming; Teng, Yueyang; Tu, Ning; Li, Ming; Liang, Guodong; Wang, Ge; Wang, Shanshan is a Medicine article available to read on EtoBox.
What is Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising about?
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.
Who reads Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising?
It is typically read by researchers, students, and practitioners in Medicine.
- Author
- Gong, Yu; Shan, Hongming; Teng, Yueyang; Tu, Ning; Li, Ming; Liang, Guodong; Wang, Ge; Wang, Shanshan
- Publisher
- Institute of Electrical and Electronics Engineers; Institute of Electrical and Electronics Engineers (IEEE) (ISSN 2469-7311)
- Published
- 2021
- Language
- EN
- Field
- Medicine (Physical Sciences)