# Deep Sinogram Completion with Image Prior for Metal Artifact Reduction in CT Images

arXiv: https://arxiv.org/abs/2009.07469 · HTML: https://mlsanity.com/paper/2009.07469/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 30 of 40)

- [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021)
- [InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images](https://mlsanity.com/paper/2112.12660/) (2021)
- [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)
- [A Geometry-Informed Deep Learning Framework for Ultra-Sparse 3D Tomographic Image Reconstruction](https://mlsanity.com/paper/2105.11692/) (2021)
- [DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2102.08003/) (2021)
- [Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction](https://mlsanity.com/paper/2306.15203/) (2023)
- [Quad-Net: Quad-domain Network for CT Metal Artifact Reduction](https://mlsanity.com/paper/2207.11678/) (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)
- [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)
- [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)
- [Nonlinear ill-posed problem in low-dose dental cone-beam computed tomography](https://mlsanity.com/paper/2303.01678/) (2023)
- [Tomographic Foundation Model -- FORCE: Flow-Oriented Reconstruction Conditioning Engine](https://mlsanity.com/paper/2506.02149/) (2025)
- [Learning-Based Patch-Wise Metal Segmentation with Consistency Check](https://mlsanity.com/paper/2101.10914/) (2021)
- [Investigation of domain gap problem in several deep-learning-based CT metal artefact reduction methods](https://mlsanity.com/paper/2111.12983/) (2021)
- [View-Consistent Metal Segmentation in the Projection Domain for Metal Artifact Reduction in CBCT -- An Investigation of Potential Improvement](https://mlsanity.com/paper/2112.02101/) (2021)
- [Deep learning based projection domain metal segmentation for metal artifact reduction in cone beam computed tomography](https://mlsanity.com/paper/2208.08288/) (2022)
- [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)
- [FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction](https://mlsanity.com/paper/2508.10617/) (2025)
- [Metal Artifact Reduction with Intra-Oral Scan Data for 3D Low Dose Maxillofacial CBCT Modeling](https://mlsanity.com/paper/2202.03571/) (2022)
- [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)
- [Neural Representation-Based Method for Metal-induced Artifact Reduction in Dental CBCT Imaging](https://mlsanity.com/paper/2307.14579/) (2023)
- [Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction](https://mlsanity.com/paper/2406.12186/) (2024)

## Abstract (arXiv)

Computed tomography (CT) has been widely used for medical diagnosis, assessment, and therapy planning and guidance. In reality, CT images may be affected adversely in the presence of metallic objects, which could lead to severe metal artifacts and influence clinical diagnosis or dose calculation in radiation therapy. In this paper, we propose a generalizable framework for metal artifact reduction (MAR) by simultaneously leveraging the advantages of image domain and sinogram domain-based MAR techniques. We formulate our framework as a sinogram completion problem and train a neural network (SinoNet) to restore the metal-affected projections. To improve the continuity of the completed projections at the boundary of metal trace and thus alleviate new artifacts in the reconstructed CT images, we train another neural network (PriorNet) to generate a good prior image to guide sinogram learning, and further design a novel residual sinogram learning strategy to effectively utilize the prior image information for better sinogram completion. The two networks are jointly trained in an end-to-end fashion with a differentiable forward projection (FP) operation so that the prior image generation and deep sinogram completion procedures can benefit from each other. Finally, the artifact-reduced CT images are reconstructed using the filtered backward projection (FBP) from the completed sinogram. Extensive experiments on simulated and real artifacts data demonstrate that our method produces superior artifact-reduced results while preserving the anatomical structures and outperforms other MAR methods.