Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss

  • Zheng Wang
  • Jianwu LiEmail author
  • Mogendi Enoh
Original Article


Cone beam computed tomography (CBCT) is an important tool for clinical diagnosis and many industrial applications. However, ring artifacts usually appear in CBCT images, due to device responding inconsistence. This paper designs a generative adversarial network (GAN) to remove ring artifacts and meanwhile to retain important texture details in CBCT images. This method firstly transforms ring artifacts in Cartesian coordinates to stripe artifacts in polar coordinates, which is very helpful for removing ring artifacts. Then, we design a new loss function for GAN, including three parts: unidirectional relative total variation loss, perceptual loss and adversarial loss. Further, inspired by super-resolution generative adversarial networks, we use very deep residual networks for both generator and discriminator. Experimental results show that the proposed method is more effective for ring artifacts removal, compared to our baseline and some traditional methods.


Cone beam computed tomography Ring artifacts Generative adversarial networks Super-resolution generative adversarial networks 



This work was supported by the National Natural Science Foundation of China (No. 61271374). The authors would like thank the anonymous reviews for their helpful suggestions which have led to great improvement on this paper, especially on the experiments.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  1. 1.Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and TechnologyBeijing Institute of TechnologyBeijingChina

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