Generative adversarial network (GAN) has been applied for low-dose CT images to predict normal-dose CT images. However, the undesired artifacts and details bring uncertainty to the clinical diagnosis. In order to improve the visual quality while suppressing the noise, in this paper, we mainly studied the two key components of deep learning based low-dose CT (LDCT) restoration models—network architecture and adversarial loss, and proposed a disentangled noise suppression method based on GAN (DNSGAN) for LDCT. Specifically, a generator network, which contains the noise suppression and structure recovery modules, is proposed. Furthermore, a multi-scaled relativistic adversarial loss is introduced to preserve the finer structures of generated images. Experiments on simulated and real LDCT datasets show that the proposed method can effectively remove noise while recovering finer details and provide better visual perception than other state-of-the-art methods.

生成对抗网络 (GAN) 已应用于低剂量 CT 图像以预测正常剂量 CT 图像。然而，不想要的伪影和细节给临床诊断带来了不确定性。为了在抑制噪声的同时提高视觉质量，本文主要研究了基于深度学习的低剂量 CT (LDCT) 恢复模型的两个关键组成部分——网络架构和对抗性损失，并提出了一种解缠结噪声抑制方法基于 GAN（DNSGAN) 为 LDCT。具体来说，提出了一个包含噪声抑制和结构恢复模块的生成器网络。此外，引入了多尺度相对论对抗性损失以保留生成图像的更精细结构。在模拟和真实 LDCT 数据集上的实验表明，与其他最先进的方法相比，所提出的方法可以有效去除噪声，同时恢复更精细的细节，并提供更好的视觉感知。