# ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

arXiv: https://arxiv.org/abs/1908.01104 · HTML: https://mlsanity.com/paper/1908.01104/

## Key facts

- Cited by 31 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 3 Aug 2019.

## Built on by (most cited first, 30 of 31)

- [Learning Disentangled Representations in the Imaging Domain](https://mlsanity.com/paper/2108.12043/) (2021)
- [Zero-shot Medical Image Translation via Frequency-Guided Diffusion Models](https://mlsanity.com/paper/2304.02742/) (2023)
- [HQG-Net: Unpaired Medical Image Enhancement with High-Quality Guidance](https://mlsanity.com/paper/2307.07829/) (2023)
- [InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2112.12660/) (2021)
- [Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2205.07471/) (2022)
- [Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration](https://mlsanity.com/paper/2312.15701/) (2023)
- [Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains](https://mlsanity.com/paper/2308.16742/) (2023)
- [DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2102.08003/) (2021)
- [Review of Disentanglement Approaches for Medical Applications -- Towards Solving the Gordian Knot of Generative Models in Healthcare](https://mlsanity.com/paper/2203.11132/) (2022)
- [Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction](https://mlsanity.com/paper/2306.15203/) (2023)
- [Quad-Net: Quad-domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2207.11678/) (2022)
- [ISCL: Interdependent Self-Cooperative Learning for Unpaired Image Denoising](https://mlsanity.com/paper/2102.09858/) (2021)
- [U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction](https://mlsanity.com/paper/2103.04552/) (2021)
- [IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2104.01405/) (2021)
- [Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning](https://mlsanity.com/paper/2109.13483/) (2021)
- [Orientation-Shared Convolution Representation for CT Metal Artifact Learning](https://mlsanity.com/paper/2212.13166/) (2022)
- [MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2306.14274/) (2023)
- [Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/2307.12717/) (2023)
- [Decomposing Normal and Abnormal Features of Medical Images into Discrete Latent Codes for Content-Based Image Retrieval](https://mlsanity.com/paper/2103.12328/) (2021)
- [Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods](https://mlsanity.com/paper/2111.12983/) (2021)
- [ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation](https://mlsanity.com/paper/2506.19531/) (2025)
- [TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2211.07190/) (2022)
- [QS-ADN: Quasi-Supervised Artifact Disentanglement Network for Low-Dose CT Image Denoising by Local Similarity Among Unpaired Data](https://mlsanity.com/paper/2302.03916/) (2023)
- [RetinexFlow for CT metal artifact reduction](https://mlsanity.com/paper/2306.10520/) (2023)
- [Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data](https://mlsanity.com/paper/2503.21259/) (2025)
- [Elucidating the Design Space of Arbitrary-Noise-Based Diffusion Models](https://mlsanity.com/paper/2507.18534/) (2025)
- [NAB: Neural Adaptive Binning for Sparse-View CT reconstruction](https://mlsanity.com/paper/2602.02356/) (2026)
- [Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.05934/) (2026)
- [GraphMAR: Geometry-Aware Graph Learning Framework for Spatially Adaptive CT Metal Artifact Reduction](https://mlsanity.com/paper/2605.17343/) (2026)
- [Splat-Based Metal Artifact Reduction in Cone-Beam CT via Compact Attenuation Modeling](https://mlsanity.com/paper/2608.04764/) (2026)

## Abstract (arXiv)

Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training. However, as synthesized data may not accurately 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 disentangles the metal artifacts from CT images in the latent space. It supports different forms of generations (artifact reduction, artifact transfer, and self-reconstruction, etc.) with specialized loss functions to obviate the need for supervision with synthesized data. Extensive experiments show that when applied to a synthesized dataset, our method addresses metal artifacts significantly better than the existing unsupervised models designed for natural image-to-image translation problems, and achieves comparable performance to existing supervised models for MAR. When applied to clinical datasets, our method demonstrates better generalization ability over the supervised models. The source code of this paper is publicly available at https://github.com/liaohaofu/adn.