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Neural Computing and Applications

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12.01.2019 | Original Article

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

verfasst von: Zheng Wang, Jianwu Li, Mogendi Enoh

Erschienen in: Neural Computing and Applications | Ausgabe 9/2019

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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.

Vorheriger Artikel From fuzzy center average defuzzifier (CAD) to fuzzy lookup table controller (FLTC) with an efficient Heaviside search algorithm (HSA)

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Titel: Removing ring artifacts in CBCT images via generative adversarial networks with unidirectional relative total variation loss
verfasst von: Zheng Wang
Jianwu Li
Mogendi Enoh
Publikationsdatum: 12.01.2019
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 9/2019
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-018-04007-6

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