# Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

arXiv: https://arxiv.org/abs/1906.01806 · HTML: https://mlsanity.com/paper/1906.01806/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2019.

## Built on by (most cited first, 3 of 3)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2104.01405/) (2021)
- [MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images](https://mlsanity.com/paper/2311.09590/) (2023)

## Abstract (arXiv)

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods which rely heavily on synthesized data for training. However, as synthesized data may not perfectly simulate the underlying physical mechanisms of CT imaging, the supervised methods often generalize poorly to clinical applications. To address this problem, we propose, to the best of our knowledge, the first unsupervised learning approach to MAR. Specifically, we introduce a novel artifact disentanglement network that enables different forms of generations and regularizations between the artifact-affected and artifact-free image domains to support unsupervised learning. Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a synthesized dataset. When applied to clinical datasets, our method achieves considerable improvements over the supervised models. The source code of this paper is publicly available at https://github.com/liaohaofu/adn.