# InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction

arXiv: https://arxiv.org/abs/2109.05298 · HTML: https://mlsanity.com/paper/2109.05298/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2021.

## Built on by (most cited first, 23 of 23)

- [InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2112.12660/) (2021)
- [KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution](https://mlsanity.com/paper/2209.10305/) (2022)
- [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)
- [Quad-Net: Quad-domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2207.11678/) (2022)
- [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)
- [A Regularization-Guided Equivariant Approach for Image Restoration](https://mlsanity.com/paper/2505.19799/) (2025)
- [MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images](https://mlsanity.com/paper/2311.09590/) (2023)
- [Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction](https://mlsanity.com/paper/2408.14342/) (2024)
- [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)
- [RetinexFlow for CT metal artifact reduction](https://mlsanity.com/paper/2306.10520/) (2023)
- [Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction](https://mlsanity.com/paper/2406.12186/) (2024)
- [Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data](https://mlsanity.com/paper/2503.21259/) (2025)
- [Diffusion Model Regularized Implicit Neural Representation for CT Metal Artifact Reduction](https://mlsanity.com/paper/2512.08999/) (2025)
- [Resolution-Agnostic Neural Operators for Multi-Rate Sparse-View CT](https://mlsanity.com/paper/2512.12236/) (2025)
- [Balancing Efficiency and Restoration: Lightweight Mamba-Based Model for CT Metal Artifact Reduction](https://mlsanity.com/paper/2604.06622/) (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)
- [Splat-based Metal Artifact Reduction in Cone-Beam CT via Polychromatic Modeling](https://mlsanity.com/paper/2608.13159/) (2026)
- [VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark](https://mlsanity.com/paper/2610.01512/) (2026)

## Abstract (arXiv)

For the task of metal artifact reduction (MAR), although deep learning (DL)-based methods have achieved promising performances, most of them suffer from two problems: 1) the CT imaging geometry constraint is not fully embedded into the network during training, leaving room for further performance improvement; 2) the model interpretability is lack of sufficient consideration. Against these issues, we propose a novel interpretable dual domain network, termed as InDuDoNet, which combines the advantages of model-driven and data-driven methodologies. Specifically, we build a joint spatial and Radon domain reconstruction model and utilize the proximal gradient technique to design an iterative algorithm for solving it. The optimization algorithm only consists of simple computational operators, which facilitate us to correspondingly unfold iterative steps into network modules and thus improve the interpretablility of the framework. Extensive experiments on synthesized and clinical data show the superiority of our InDuDoNet. Code is available in urlhttps://github.com/hongwang01/InDuDoNet.%method on the tasks of MAR and downstream multi-class pelvic fracture segmentation.