# InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images

arXiv: https://arxiv.org/abs/2112.12660 · HTML: https://mlsanity.com/paper/2112.12660/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 47 references that are in the map.
- First posted on arXiv 23 Dec 2021.

## Builds on (most cited first, 17 of 17)

- [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)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Deep Unfolding Network for Image Super-Resolution](https://mlsanity.com/paper/2003.10428/) (2020)
- [A Model-driven Deep Neural Network for Single Image Rain Removal](https://mlsanity.com/paper/2005.01333/) (2020)
- [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)
- [Multispectral and Hyperspectral Image Fusion by MS/HS Fusion Net](https://mlsanity.com/paper/1901.03281/) (2019)
- [ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/1908.01104/) (2019)
- [ADMM-Net: A Deep Learning Approach for Compressive Sensing MRI](https://mlsanity.com/paper/1705.06869/) (2017)
- [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)
- [Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction](https://mlsanity.com/paper/1907.00294/) (2019)
- [RCDNet: An Interpretable Rain Convolutional Dictionary Network for Single Image Deraining](https://mlsanity.com/paper/2107.06808/) (2021)
- [A Re-weighted Joint Spatial-Radon Domain CT Image Reconstruction Model for Metal Artifact Reduction](https://mlsanity.com/paper/1707.07297/) (2017)
- [Sinogram constrained TV-minimization for metal artifact reduction in CT](https://mlsanity.com/paper/1404.6691/) (2014)

## Built on by (most cited first, 19 of 19)

- [RPCANet: Deep Unfolding RPCA Based Infrared Small Target Detection](https://mlsanity.com/paper/2311.00917/) (2023)
- [Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains](https://mlsanity.com/paper/2308.16742/) (2023)
- [RPCANet++: Deep Interpretable Robust PCA for Sparse Object Segmentation](https://mlsanity.com/paper/2508.04190/) (2025)
- [Robust Stochastically-Descending Unrolled Networks](https://mlsanity.com/paper/2312.15788/) (2023)
- [Dense Transformer based Enhanced Coding Network for Unsupervised Metal Artifact Reduction](https://mlsanity.com/paper/2307.12717/) (2023)
- [Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts in 3D X-Ray CBCT](https://mlsanity.com/paper/2412.05853/) (2024)
- [ReMAR-DS: Recalibrated Feature Learning for Metal Artifact Reduction and CT Domain Transformation](https://mlsanity.com/paper/2506.19531/) (2025)
- [Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data](https://mlsanity.com/paper/2503.21259/) (2025)
- [Prompt Guiding Multi-Scale Adaptive Sparse Representation-driven Network for Low-Dose CT MAR](https://mlsanity.com/paper/2504.19687/) (2025)
- [Elucidating the Design Space of Arbitrary-Noise-Based Diffusion Models](https://mlsanity.com/paper/2507.18534/) (2025)
- [Multi-needle Localization for Pelvic Seed Implant Brachytherapy based on Tip-handle Detection and Matching](https://mlsanity.com/paper/2509.17931/) (2025)
- [Diffusion Model Regularized Implicit Neural Representation for CT Metal Artifact Reduction](https://mlsanity.com/paper/2512.08999/) (2025)
- [Physically-Grounded Manifold Projection Model for Generalizable Metal Artifact Reduction in Dental CBCT](https://mlsanity.com/paper/2512.24260/) (2025)
- [Unrolled Neural Networks for Constrained Optimization](https://mlsanity.com/paper/2601.17274/) (2026)
- [AS-Mamba: Asymmetric Self-Guided Mamba Decoupled Iterative Network for Metal Artifact Reduction](https://mlsanity.com/paper/2602.06350/) (2026)
- [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)
- [SCMA: Structure-Conditioned and Metal-Aware Flow Matching for CT Metal Artifact Reduction](https://mlsanity.com/paper/2607.28759/) (2026)
- [VoxelSynth3D: Interpretable Volumetric Image-Domain Metal Artifact Reduction with a Paired Synthetic CLINIC Benchmark](https://mlsanity.com/paper/2610.01512/) (2026)

## Abstract (arXiv)

During the computed tomography (CT) imaging process, metallic implants within patients often cause harmful artifacts, which adversely degrade the visual quality of reconstructed CT images and negatively affect the subsequent clinical diagnosis. For the metal artifact reduction (MAR) task, current deep learning based methods have achieved promising performance. However, most of them share two main common limitations: 1) the CT physical imaging geometry constraint is not comprehensively incorporated into deep network structures; 2) the entire framework has weak interpretability for the specific MAR task; hence, the role of each network module is difficult to be evaluated. To alleviate these issues, in the paper, we construct a novel deep unfolding dual domain network, termed InDuDoNet+, into which CT imaging process is finely embedded. Concretely, we derive a joint spatial and Radon domain reconstruction model and propose an optimization algorithm with only simple operators for solving it. By unfolding the iterative steps involved in the proposed algorithm into the corresponding network modules, we easily build the InDuDoNet+ with clear interpretability. Furthermore, we analyze the CT values among different tissues, and merge the prior observations into a prior network for our InDuDoNet+, which significantly improve its generalization performance.Comprehensive experiments on synthesized data and clinical data substantiate the superiority of the proposed methods as well as the superior generalization performance beyond the current state-of-the-art (SOTA) MAR methods. . Code is available at urlhttps://github.com/hongwang01/InDuDoNetplus.