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

• URL: http://arxiv.org/abs/2104.12510v1
• Date: Mon, 26 Apr 2021 12:22:56 GMT
• Title: Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks
• Authors: Wang Zihao, Vandersteen Clair, Demarcy Thomas, Gnansia Dan, Raffaelli Charles, Guevara Nicolas, Delingette Herve
• Abstract summary: Metal artifacts create difficulties for a high quality visual assessment of post-operative imaging in computed tomography. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Metal Artifacts creates often difficulties for a high quality visual assessment of post-operative imaging in {c}omputed {t}omography (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 a 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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