A novel super-resolution CT image reconstruction via semi-supervised generative adversarial network

Abstract

Reconstruction of super-resolution CT images using deep learning requires a large number of high-resolution images. However, high-resolution images are often limited to access due to CT performance and operation factors. In this paper, a new semi-supervised generative adversarial network is presented to accurately recover high-resolution CT images from low-resolution counterparts. We use a deep unsupervised network of 16 residual blocks to design the generator and build a discriminator based on a supervised network. We also apply a parallel 1 × 1 convolution operation to reduce the dimensionality of each hidden layer’s output. Four types of loss functions are presented to build a new one for enforcing the mappings between the generator and discriminator. The bulk specification layer in the commonly used residual network is removed to construct a new type of residual network. In terms of experiments, we conduct an objective and subjective comprehensive evaluation with several state-of-the-art methods. The comparison results show that our proposed network has better advantages in super-resolution image reconstruction.