Abstract
To assess the quality of insertion of Cochlear Implants (CI) after surgery, it is important to analyze the positions of the electrodes with respect to the cochlea based on post-operative CT imaging. Yet, these images suffer from metal artifacts which often entail a difficulty to make any analysis. In this work, we propose a 3D metal artifact reduction method using convolutional neural networks for post-operative cochlear implant imaging. Our approach is based on a 3D generative adversarial network (MARGANs) to create an image with a reduction of metal artifacts. The generative model is trained on a large number of pre-operative “artifact-free” images on which simulated metal artifacts are created. This simulation involves the segmentation of the scala tympani, the virtual insertion of electrode arrays and the simulation of beam hardening based on the Beer-Lambert law.
Quantitative and qualitative evaluations compared with two classical metallic artifact reduction algorithms show the effectiveness of our method.
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Acknowledgements
This work was partially funded by the regional council of Provence Alpes Côte d’Azur, by the French government through the UCA\(^{\mathrm {JEDI}}\) “Investments in the Future” project managed by the National Research Agency (ANR) with the reference number ANR-15-IDEX-01, and was supported by the grant AAP Santé 06 2017-260 DGA-DSH.
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Wang, Z. et al. (2019). Deep Learning Based Metal Artifacts Reduction in Post-operative Cochlear Implant CT Imaging. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_14
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DOI: https://doi.org/10.1007/978-3-030-32226-7_14
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