Inner-ear augmented metal artifact reduction with simulation-based 3D generative adversarial networks. (October 2021)

• Record Type: Journal Article
• Authors: Wang, Zihao; Vandersteen, Clair; Demarcy, Thomas; Gnansia, Dan; Raffaelli, Charles; Guevara, Nicolas; Delingette, Hervé
• Is Part Of: Computerized medical imaging and graphics. Volume 93(2021)
• Publication Date: 2021-10
• Subjects: Artifact reduction -- Deep learning -- GAN; Diagnostic imaging -- Periodicals; Imaging systems in medicine -- Periodicals; Diagnosis, Radioscopic -- Data processing -- Periodicals; Diagnostic Imaging -- Periodicals; Imagerie pour le diagnostic -- Périodiques; Diagnostic imaging; Periodicals; Electronic journals; Electronic journals; 616.0754
• DOI: 10.1016/j.compmedimag.2021.101990
• Languages: English
• ISSNs: 0895-6111

Abstract

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 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.