  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.
