# ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction

## TL;DR

This paper introduces an unsupervised artifact disentanglement network for CT metal artifact reduction, which outperforms existing unsupervised methods and generalizes better to clinical data without relying on synthesized training data.

## Contribution

The paper presents the first unsupervised learning approach for CT metal artifact reduction using artifact disentanglement in the latent space, eliminating the need for synthesized training data.

## Key findings

- Outperforms existing unsupervised models on synthetic data
- Achieves comparable results to supervised models on MAR
- Demonstrates superior generalization on clinical datasets

## Abstract

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.