Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.
Parameter-Transferred Wasserstein Generative Adversarial Network (PT-WGAN) for Low-Dose PET Image Denoising.
复制标题
用于低剂量PET图像去噪的参数转移Wasserstein生成对抗网络（PT-WGAN）。
DOI：
10.1109/trpms.2020.3025071
复制
发表时间：
2021-03
影响因子：
4.4
通讯作者：
Wang S
中科院分区：
文献类型：
--
作者：
Gong Y;Shan H;Teng Y;Tu N;Li M;Liang G;Wang G;Wang S
由于正电子发射断层扫描（PET）在临床实践中的广泛应用，需要将PET相关辐射剂量对患者的潜在风险降到最低。然而，随着辐射剂量的降低，所得到的图像可能会受到噪声和伪影的影响，从而影响诊断性能。本文提出了一种用于低剂量PET图像去噪的参数转移Wasserstein生成对抗网络（PT-WGAN）。本文的贡献有两个：i)设计了一个PT-WGAN框架，在不影响结构细节的情况下对低剂量PET图像进行降噪；ii)开发了一个基于迁移学习的任务特定初始化，使用从预训练模型转移的可训练参数来训练PT-WGAN，这大大提高了PT-WGAN的训练效率。临床数据的实验结果表明，与最近发表的最先进的方法相比，所提出的网络可以更有效地抑制图像噪声，同时保持更好的图像保真度。我们在https://github.com/90n9-yu/PT-WGAN上提供了我们的代码。
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.