CT image super-resolution under the guidance of deep gradient information.

Abstract

Due to the hardware constraints of Computed Tomography (CT) imaging, acquiring high-resolution (HR) CT images in clinical settings poses a significant challenge. In recent years, convolutional neural networks have shown great potential in CT super-resolution (SR) problems. However, the reconstruction results of many deep learning-based SR methods have structural distortion and detail ambiguity. In this paper, a new SR network based on generative adversarial learning is proposed. The network consists of gradient branch and SR branch. Gradient branch is used to recover HR gradient maps. The network merges gradient image features of the gradient branch into the SR branch, offering gradient information guidance for super-resolution (SR) reconstruction. Further, the loss function of the network combines the image space loss function with the gradient loss and the gradient variance loss to further generate a more realistic detail texture. Compared to other comparison algorithms, the structural similarity index of the SR results obtained by the proposed method on simulation and experimental data has increased by 1.8% and 1.4%, respectively. The experimental results demonstrate that the proposed CT SR network exhibits superior performance in terms of structure preservation and detail restoration.