Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear

@article{Wang2018ConditionalGA,
  title={Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear},
  author={J. Wang and Yiyuan Zhao and Jack H. Noble and Benoit M. Dawant},
  journal={Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention},
  year={2018},
  volume={11070},
  pages={
          3-11
        }
}
  • J. Wang, Yiyuan Zhao, B. Dawant
  • Published 16 September 2018
  • Computer Science
  • Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
We propose an approach based on a conditional generative adversarial network (cGAN) for the reduction of metal artifacts (RMA) in computed tomography (CT) ear images of cochlear implants (CIs) recipients. Our training set contains paired pre-implantation and post-implantation CTs of 90 ears. At the training phase, the cGAN learns a mapping from the artifact-affected CTs to the artifact-free CTs. At the inference phase, given new metal-artifact-affected CTs, the cGAN produces CTs in which the… 
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