# DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

arXiv: https://arxiv.org/abs/2102.08003 · HTML: https://mlsanity.com/paper/2102.08003/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 74 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Deep Convolutional Neural Network for Inverse Problems in Imaging](https://mlsanity.com/paper/1611.03679/) (2016)
- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database](https://mlsanity.com/paper/1711.10535/) (2017)
- [Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2009.07469/) (2020)
- [ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/1908.01104/) (2019)
- [CT sinogram-consistency learning for metal-induced beam hardening correction](https://mlsanity.com/paper/1708.00607/) (2017)
- [Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms](https://mlsanity.com/paper/2007.13290/) (2020)
- [Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction](https://mlsanity.com/paper/1907.00294/) (2019)
- [A Re-weighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction](https://mlsanity.com/paper/1707.07297/) (2017)
- [Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting](https://mlsanity.com/paper/1911.05530/) (2019)

## Built on by (most cited first, 10 of 10)

- [Metal Inpainting in CBCT Projections Using Score-based Generative Model](https://mlsanity.com/paper/2209.09733/) (2022)
- [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)
- [Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/2307.12717/) (2023)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [Confidence-Guided Unsupervised Domain Adaptation for Cerebellum Segmentation](https://mlsanity.com/paper/2206.10357/) (2022)
- [TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2211.07190/) (2022)
- [Metal-conscious Embedding for CBCT Projection Inpainting](https://mlsanity.com/paper/2211.16219/) (2022)

## Abstract (arXiv)

Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results achieved by these methods, there is still much room to further improve performance. In this paper, a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for MAR. We correct the corrupted sinogram using adaptive scaling first to preserve more tissue and bone details as a more informative input. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and nonlocal module are leveraged in the proposed network model. In the experiments, the proposed DAN-Net demonstrates performance competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects.