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

TL;DR

This paper introduces a 3D generative adversarial network trained on simulated CT artifacts to effectively reduce metal artifacts in high-resolution post-operative cochlear implant images, outperforming existing methods.

Contribution

The study presents a novel simulation-based training approach for 3D GANs specifically designed for metal artifact reduction in high-resolution CT scans.

Findings

Outperforms existing metal artifact reduction methods

Effective on clinical conventional and cone-beam CT images

Improves visual assessment of post-operative imaging

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.