High-resolution computed tomography (CT) can provide accurate diagnostic information for clinical applications. However, using CT scanning equipment to obtain high-resolution CT directly may cause significant radiation damage to human body. Low-dose CT super-resolution using generative adversarial network (GAN) can improve the visual quality of CT while maintaining a low radiation dose to human body. The existing GAN networks for super-resolution still suffer from the issues such as weak feature expression and network redundancy. This work proposes a novel lightweight multiple dense residual block structure-based GAN network for CT super-resolution. The new structure reduces the number of residual units and establishes a dense link among all residual blocks, which can reduce network redundancy and ensure maximum information transmission. In addition, in order to avoid the gradient vanishing phenomena, the Wasserstein distance is introduced into the loss function. Experimental results show that the presented method achieved a more desirable visual quality with more high-frequency details for different upscaling factors than other popular methods did.

中文翻译：

---

使用基于多密集残差块的 GAN 的 CT 超分辨率

高分辨率计算机断层扫描 (CT) 可为临床应用提供准确的诊断信息。但是，使用CT扫描设备直接获取高分辨率CT可能会对人体造成显着的辐射损伤。使用生成对抗网络 (GAN) 的低剂量 CT 超分辨率可以提高 CT 的视觉质量，同时保持对人体的低辐射剂量。现有的用于超分辨率的 GAN 网络仍然存在特征表达弱和网络冗余等问题。这项工作提出了一种新的基于轻量级多密集残差块结构的 GAN 网络，用于 CT 超分辨率。新的结构减少了残差单元的数量，并在所有残差块之间建立了一个密集的链接，可以减少网络冗余，保证信息的最大传输。另外，为了避免梯度消失现象，在损失函数中引入了Wasserstein距离。实验结果表明，与其他流行方法相比，所提出的方法针对不同的放大因子实现了更理想的视觉质量和更多的高频细节。