Deep learning based Metal Artifact Reduction in X-ray Computed Tomography

Abstract

Due to the presence of metal implants, computed tomography(CT) images of patients undergoing CT scans produce severe metal artifacts. In recent years, metal artifact reduction (MAR) algorithms have been developed at a high speed, and deep learning-based MAR algorithms have proved to be one of the very effective methods. However, based on the fact that most of the current deep learning-based solutions utilize simulated data for supervised training, these models are difficult to be directly applied in clinical settings. In addition, current MAR schemes still face considerable challenges in reduce streak artifacts and dark band artifacts from metal artifacts. In this paper, we propose an improved unsupervised network model based on CycleGAN suitable for small-sample training of medical images. In order to enhance the extraction of image features, the generator in this paper adopts the U-Net network model, which is commonly used in medical image processing, and the residual connection and attention modules are added between each layer of the U-Net network. In addition, in order to better repair the dark band artifacts, the input of the network is processed by mixing the original image and the prior image, and in order to make the output image retain as much as possible the effective information of the original image, this paper adds the correlation constraints of the prior image and the original image to the output of the network. Experimentally, it is proved that the method in this paper has very good ability to reduce metal artifacts.