# IDOL-Net: An Interactive Dual-Domain Parallel Network for CT Metal Artifact Reduction

arXiv: https://arxiv.org/abs/2104.01405 · HTML: https://mlsanity.com/paper/2104.01405/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 47 references that are in the map.
- First posted on arXiv 3 Apr 2021.

## Builds on (most cited first, 18 of 18)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Non-local Neural Networks](https://mlsanity.com/paper/1711.07971/) (2017)
- [Image Inpainting for Irregular Holes Using Partial Convolutions](https://mlsanity.com/paper/1804.07723/) (2018)
- [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)
- [MAGIC: Manifold and Graph Integrative Convolutional Network for Low-Dose CT Reconstruction](https://mlsanity.com/paper/2008.00406/) (2020)
- [Convolutional Sparse Coding for Compressed Sensing CT Reconstruction](https://mlsanity.com/paper/1903.08549/) (2019)
- [Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction](https://mlsanity.com/paper/1907.00294/) (2019)
- [Low-dimensional Manifold Constrained Disentanglement Network for Metal Artifact Reduction](https://mlsanity.com/paper/2007.03882/) (2020)
- [DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2102.08003/) (2021)
- [Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/1906.01806/) (2019)
- [Multi-domain CT Metal Artifacts Reduction Using Partial Convolution Based Inpainting](https://mlsanity.com/paper/1911.05530/) (2019)
- [A deep network for sinogram and CT image reconstruction](https://mlsanity.com/paper/2001.07150/) (2020)
- [Projection Inpainting Using Partial Convolution for Metal Artifact Reduction](https://mlsanity.com/paper/2005.00762/) (2020)

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

- [Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction](https://mlsanity.com/paper/2501.15610/) (2025)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods](https://mlsanity.com/paper/2111.12983/) (2021)
- [Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction](https://mlsanity.com/paper/2408.14342/) (2024)
- [TriDoNet: A Triple Domain Model-driven Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2211.07190/) (2022)
- [Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.06622/) (2026)

## Abstract (arXiv)

Due to the presence of metallic implants, the imaging quality of computed tomography (CT) would be heavily degraded. With the rapid development of deep learning, several network models have been proposed for metal artifact reduction (MAR). Since the dual-domain MAR methods can leverage the hybrid information from both sinogram and image domains, they have significantly improved the performance compared to single-domain methods. However,current dual-domain methods usually operate on both domains in a specific order, which implicitly imposes a certain priority prior into MAR and may ignore the latent information interaction between both domains. To address this problem, in this paper, we propose a novel interactive dualdomain parallel network for CT MAR, dubbed as IDOLNet. Different from existing dual-domain methods, the proposed IDOL-Net is composed of two modules. The disentanglement module is utilized to generate high-quality prior sinogram and image as the complementary inputs. The follow-up refinement module consists of two parallel and interactive branches that simultaneously operate on image and sinogram domain, fully exploiting the latent information interaction between both domains. The simulated and clinical results demonstrate that the proposed IDOL-Net outperforms several state-of-the-art models in both qualitative and quantitative aspects.