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

arXiv: https://arxiv.org/abs/2104.12510 · HTML: https://mlsanity.com/paper/2104.12510/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 36 references that are in the map.
- First posted on arXiv 26 Apr 2021.

## Builds on (most cited first, 5 of 5)

- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [Brain MRI super-resolution using 3D generative adversarial networks](https://mlsanity.com/paper/1812.11440/) (2018)
- [Automatically Segmenting the Left Atrium from Cardiac Images Using Successive 3D U-Nets and a Contour Loss](https://mlsanity.com/paper/1812.02518/) (2018)
- [A Deep Learning based Fast Signed Distance Map Generation](https://mlsanity.com/paper/2005.12662/) (2020)
- [Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography](https://mlsanity.com/paper/2001.00340/) (2020)

## Built on by (most cited first, 4 of 4)

- [Artifact Reduction in 3D and 4D Cone-beam Computed Tomography Images with Deep Learning -- A Review](https://mlsanity.com/paper/2403.18565/) (2024)
- [Artificial Intelligence for Cochlear Implants: Review of Strategies, Challenges, and Perspectives](https://mlsanity.com/paper/2403.15442/) (2024)
- [Attention for Image Registration (AiR): an unsupervised Transformer approach](https://mlsanity.com/paper/2105.02282/) (2021)
- [Diffusion based Zero-shot Medical Image-to-Image Translation for Cross Modality Segmentation](https://mlsanity.com/paper/2404.01102/) (2024)

## Abstract (arXiv)

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.