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2018 | OriginalPaper | Chapter

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

Authors : Jianing Wang, Yiyuan Zhao, Jack H. Noble, Benoit M. Dawant

Published in: Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

Publisher: Springer International Publishing

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Abstract

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 artifacts are removed. As a pre-processing step, we also propose a band-wise normalization method, which splits a CT image into three channels according to the intensity value of each voxel and we show that this method improves the performance of the cGAN. We test our cGAN on post-implantation CTs of 74 ears and the quality of the artifact-corrected images is evaluated quantitatively by comparing the segmentations of intra-cochlear anatomical structures, which are obtained with a previously published method, in the real pre-implantation and the artifact-corrected CTs. We show that the proposed method leads to an average surface error of 0.18 mm which is about half of what could be achieved with a previously proposed technique.
Literature
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Metadata
Title
Conditional Generative Adversarial Networks for Metal Artifact Reduction in CT Images of the Ear
Authors
Jianing Wang
Yiyuan Zhao
Jack H. Noble
Benoit M. Dawant
Copyright Year
2018
DOI
https://doi.org/10.1007/978-3-030-00928-1_1

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