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Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks

Abstract : Metal Artifacts creates often difficulties for a high-quality visual assessment of post-operative imaging in computed tomography (CT). A vast body of methods have been proposed to tackle this issue, but these methods were designed for regular CT scans and their performance is usually insufficient when imaging tiny implants. In the context of post-operative high-resolution CT imaging, we propose a 3D metal artifact reduction algorithm based on a generative adversarial neural network. It is based on the simulation of physically realistic CT metal artifacts created by cochlea implant electrodes on preoperative images. The generated images serve to train 3D generative adversarial networks for artifacts reduction. The proposed approach was assessed qualitatively and quantitatively on clinical conventional and cone-beam CT of cochlear implant postoperative images. These experiments show that the proposed method outperforms other general metal artifact reduction approaches.
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https://hal.inria.fr/hal-03351225
Contributor : ZIHAO WANG Connect in order to contact the contributor
Submitted on : Wednesday, September 22, 2021 - 9:58:47 AM
Last modification on : Tuesday, August 2, 2022 - 4:24:16 AM
Long-term archiving on: : Thursday, December 23, 2021 - 6:23:14 PM

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Zihao Wang, Clair Vandersteen, Thomas Demarcy, Dan Gnansia, Charles Raffaelli, et al.. Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks. Computerized Medical Imaging and Graphics, In press, 93, pp.101990. ⟨10.1016/j.compmedimag.2021.101990⟩. ⟨hal-03351225⟩

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