@inproceedings{0b24120aa72144dc9aa5cfdb59cd558a,
title = "Removing Ring Artifacts in Cbct Images Via Generative Adversarial Network",
abstract = "Cone-beam computed tomography (CBCT) images often have some ring artifacts because of the inconsistent response of detector pixels. Removing ring artifacts in CBCT images without impairing the image quality is critical for the application of CBCT. In this paper, we explore this issue as an 'adversarial problem' and propose a novel method to eliminate ring artifacts from CBCT images by using an image-to-image network based on Generative Adversarial Network (GAN). Through combining the generative adversarial loss and the proposed smooth loss, both of the generator and the discriminator can be trained to remove ring artifacts in CBCT images by means of image-to-image. Experimental results demonstrate that the proposed method is more effective on both simulated data and real-world CBCT images, compared with other algorithms.",
keywords = "CBCT images, Generative adversarial network (GAN), Generative adversarialloss, Ring artifacts",
author = "Shuyang Zhao and Jianwu Li and Qirun Huo",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 ; Conference date: 15-04-2018 Through 20-04-2018",
year = "2018",
month = sep,
day = "10",
doi = "10.1109/ICASSP.2018.8462316",
language = "English",
isbn = "9781538646588",
series = "ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "1055--1059",
booktitle = "2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings",
address = "United States",
}