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Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging

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 ad-versarial 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 tym-pani, 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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Submitted on : Monday, July 29, 2019 - 9:32:25 AM
Last modification on : Saturday, June 25, 2022 - 11:37:46 PM


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  • HAL Id : hal-02196557, version 1


Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, et al.. Deep Learning based Metal Artifacts Reduction in post-operative Cochlear Implant CT Imaging. MICCAI 2019 - 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, Oct 2019, Shenzhen, China. pp.121-129. ⟨hal-02196557⟩



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