Removing Ring Artifacts in Cbct Images Via Generative Adversarial Network

Shuyang Zhao; Jianwu Li; Qirun Huo

ICASSP 2018accepted0 citations

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.

BibTeX

@inproceedings{icassp2018_removingringarti,
  title = {Removing Ring Artifacts in Cbct Images Via Generative Adversarial Network},
  author = {Shuyang Zhao and Jianwu Li and Qirun Huo},
  booktitle = {ICASSP 2018},
  year = {2018}
}