# Orientation-Shared Convolution Representation for CT Metal Artifact Learning

arXiv: https://arxiv.org/abs/2212.13166 · HTML: https://mlsanity.com/paper/2212.13166/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 27 references that are in the map.
- First posted on arXiv 26 Dec 2022.

## Builds on (most cited first, 11 of 11)

- [DuDoNet: Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/1907.00273/) (2019)
- [Deep Learning to Segment Pelvic Bones: Large-scale CT Datasets and Baseline Models](https://mlsanity.com/paper/2012.08721/) (2020)
- [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)
- [Fourier Series Expansion Based Filter Parametrization for Equivariant Convolutions](https://mlsanity.com/paper/2107.14519/) (2021)
- [InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2109.05298/) (2021)
- [CT sinogram-consistency learning for metal-induced beam hardening correction](https://mlsanity.com/paper/1708.00607/) (2017)
- [Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2205.07471/) (2022)
- [Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction](https://mlsanity.com/paper/1907.00294/) (2019)
- [DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2102.08003/) (2021)

## Built on by (most cited first, 6 of 6)

- [Rotation Equivariant Proximal Operator for Deep Unfolding Methods in Image Restoration](https://mlsanity.com/paper/2312.15701/) (2023)
- [Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/2307.12717/) (2023)
- [A Regularization-Guided Equivariant Approach for Image Restoration](https://mlsanity.com/paper/2505.19799/) (2025)
- [Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data](https://mlsanity.com/paper/2503.21259/) (2025)
- [Leveraging Image Editing Foundation Models for Data-Efficient CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.05934/) (2026)
- [Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.06622/) (2026)

## Abstract (arXiv)

During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment. Against this metal artifact reduction (MAR) task, the existing deep-learning-based methods have gained promising reconstruction performance. Nevertheless, there is still some room for further improvement of MAR performance and generalization ability, since some important prior knowledge underlying this specific task has not been fully exploited. Hereby, in this paper, we carefully analyze the characteristics of metal artifacts and propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts, i.e., rotationally symmetrical streaking patterns. The proposed method rationally adopts Fourier-series-expansion-based filter parametrization in artifact modeling, which can better separate artifacts from anatomical tissues and boost the model generalizability. Comprehensive experiments executed on synthesized and clinical datasets show the superiority of our method in detail preservation beyond the current representative MAR methods. Code will be available at urlhttps://github.com/hongwang01/OSCNet