Ring artifact is one of the most common artifacts in various types of CT （Computed Tomography） images， which is usually caused by the inconsistent response of detector pixels to X-rays. Effective removal of ring artifacts， which is a necessary step in CT image reconstruction， will greatly improve the quality of CT images and enhance the accuracy of later diagnosis and analysis. Therefore， the methods of ring artifact removal （also known as ring artifact correction） were systematically reviewed. Firstly， the performance and causes of ring artifacts were introduced， and commonly used datasets and algorithm libraries were given. Secondly， ring artifact removal methods were divided into three categories to introduce. The first category was based on detector calibration. The second category was based on analytical and iterative solution， including projection data preprocessing， CT image reconstruction and CT image post-processing. The last category was based on deep learning methods such as convolutional neural network and generative adversarial network. The principle， development process， advantages and limitations of each method were analyzed. Finally， the technical bottlenecks of existing ring artifact removal methods in terms of robustness， dataset diversity and model construction were summarized， and the solutions were prospected.

Sparse-view projection can reduce the scan does and scan time of Cone-Beam Computed Tomography （CBCT） effectively but brings a lot of streak artifacts to the reconstructed images. Sinogram inpainting can generate projection data for missing angles and improve the quality of reconstructed images. Based on the above， a Residual Encoder-Decoder Generative Adversarial Network （RED-GAN） was proposed for sinogram inpainting to reconstruct sparse-view CBCT images. In this network， the U-Net generator in Pix2pixGAN （Pix2pix Generative Adversarial Network） was replaced with the Residual Encoder-Decoder （RED） module. In addition， the conditional discriminator based on PatchGAN （Patch Generative Adversarial Network） was used to distinguish between the repaired sinograms from the real sinograms， thereby further improving the network performance. After the network training using real CBCT projection data， the proposed network was tested under 1/2， 1/3 and 1/4 sparse-view sampling conditions， and compared with linear interpolation method， Residual Encoder-Decoder Convolutional Neural Network （RED-CNN） and Pix2pixGAN. Experimental results indicate that the sinogram inpainting results of RED-GAN are better than those of the comparison methods under all the three conditions. Under the 1/4 sparse-view sampling condition， the proposed network has the most obvious advantages. In the sinogram domain， the proposed network has the Root Mean Square Error （RMSE） decreased by 7.2%， Peak Signal-to-Noise Ratio （PSNR） increased by 1.5% and Structural Similarity （SSIM） increased by 1.4%； in the reconstructed image domain， the proposed network has the RMSE decreased by 5.4%， PSNR increased by 1.6% and SSIM increased by 1.0%. It can be seen that RED-GAN is suitable for high-quality CBCT reconstruction and has potential application value in the field of fast low-dose CBCT scanning.