Adversarial Sparse-View CBCT Artifact Reduction

  • Haofu LiaoEmail author
  • Zhimin Huo
  • William J. Sehnert
  • Shaohua Kevin Zhou
  • Jiebo Luo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11070)


We present an effective post-processing method to reduce the artifacts from sparsely reconstructed cone-beam CT (CBCT) images. The proposed method is based on the state-of-the-art, image-to-image generative models with a perceptual loss as regulation. Unlike the traditional CT artifact-reduction approaches, our method is trained in an adversarial fashion that yields more perceptually realistic outputs while preserving the anatomical structures. To address the streak artifacts that are inherently local and appear across various scales, we further propose a novel discriminator architecture based on feature pyramid networks and a differentially modulated focus map to induce the adversarial training. Our experimental results show that the proposed method can greatly correct the cone-beam artifacts from clinical CBCT images reconstructed using 1/3 projections, and outperforms strong baseline methods both quantitatively and qualitatively.



The work presented here was supported in part by New York State through the Goergen Institute for Data Science at the University of Rochester and the corporate sponsor Carestream Health Inc.


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Haofu Liao
    • 1
    Email author
  • Zhimin Huo
    • 1
  • William J. Sehnert
    • 2
  • Shaohua Kevin Zhou
    • 3
  • Jiebo Luo
    • 1
  1. 1.Department of Computer ScienceUniversity of RochesterRochesterUSA
  2. 2.Carestream Health Inc.RochesterUSA
  3. 3.Institute of Computing Technology, Chinese Academy of SciencesBeijingChina

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