Super-Resolution Reconstruction of Sonograms Using Residual Dense Conditional Generative Adversarial Network

A method for super-resolution reconstruction of sonograms based on Residual Dense Conditional Generative Adversarial Network (RDC-GAN) is proposed in this paper. It is well known that the resolution of medical ultrasound images is limited, and the single-frame image super-resolution algorithms based on a convolutional neural network are prone to losing texture details, extracting much fewer features, and then blurring the reconstructed images. Therefore, it is very important to reconstruct high-resolution medical images in terms of retaining textured details. A Generative Adversarial Network could learn the mapping relationship between low-resolution and high-resolution images. Based on GAN, a new network is designed, where the generation network is composed of dense residual modules. On the one hand, low-resolution (LR) images are input into the dense residual network, then the multi-level features of images are learned, and then are fused into the global residual features. On the other hand, conditional variables are introduced into a discriminator network to guide the process of super-resolution image reconstruction. The proposed method could realize four times magnification reconstruction of medical ultrasound images. Compared with classical algorithms including Bicubic, SRGAN, and SRCNN, experimental results show that the super-resolution effect of medical ultrasound images based on RDC-GAN could be effectively improved, both in objective numerical evaluation and subjective visual assessment. Moreover, the application of super-resolution reconstructed images to stage the diagnosis of cirrhosis is discussed and the accuracy rates prove the practicality in contrast to the original images.