CT super-resolution using multiple dense residual block based GAN

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.