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.
@article{arxiv.2104.12510,
title = {Inner-ear Augmented Metal Artifact Reduction with Simulation-based 3D Generative Adversarial Networks},
author = {Wang Zihao and Vandersteen Clair and Demarcy Thomas and Gnansia Dan and Raffaelli Charles and Guevara Nicolas and Delingette Herve},
journal= {arXiv preprint arXiv:2104.12510},
year = {2021}
}