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

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Abstract

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.

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Acknowledgements

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.

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  1. Beijing Key Laboratory of Intelligent Information Technology, School of Computer Science and Technology, Beijing Institute of Technology, Beijing, 100081, China

    Zheng Wang, Jianwu Li & Mogendi Enoh

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  1. Zheng Wang
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  2. Jianwu Li
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  3. Mogendi Enoh
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Correspondence to Jianwu Li.

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Wang, Z., Li, J. & Enoh, M. Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss. Neural Comput & Applic 31, 5147–5158 (2019). https://doi.org/10.1007/s00521-018-04007-6

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