# Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains arXiv: https://arxiv.org/abs/2308.16742 ยท HTML: https://mlsanity.com/paper/2308.16742/ ## Key facts - Cited by 12 papers in the map (data through 14 Oct 2026). - Placed in Medical imaging and segmentation, one of 14 directions in Diffusion models. - Builds on 23 of its 38 references that are in the map. - First posted on arXiv 31 Aug 2023. ## Placed in - Diffusion models / [Medical imaging and segmentation](https://mlsanity.com/diffusion/medical-imaging-and-segmentation/) ## Builds on (most cited first, 23 of 23) - [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020) - [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020) - [Score-Based Generative Modeling through Stochastic Differential Equations](https://mlsanity.com/paper/2011.13456/) (2020) - [Diffusion Models Beat GANs on Image Synthesis](https://mlsanity.com/paper/2105.05233/) (2021) - [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022) - [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016) - [Glow: Generative Flow with Invertible 1x1 Convolutions](https://mlsanity.com/paper/1807.03039/) (2018) - [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014) - [Diffusion Models in Vision: A Survey](https://mlsanity.com/paper/2209.04747/) (2022) - [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018) - [Solving Inverse Problems in Medical Imaging with Score-Based Generative Models](https://mlsanity.com/paper/2111.08005/) (2021) - [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) - [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) - [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) - [Improving Diffusion Model Efficiency Through Patching](https://mlsanity.com/paper/2207.04316/) (2022) - [Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction](https://mlsanity.com/paper/2306.15203/) (2023) - [U-DuDoNet: Unpaired dual-domain network for CT metal artifact reduction](https://mlsanity.com/paper/2103.04552/) (2021) ## Built on by (most cited first, 12 of 12) - [Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction](https://mlsanity.com/paper/2501.15610/) (2025) - [Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction](https://mlsanity.com/paper/2408.14342/) (2024) - [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) - [ALL-PET: A Low-resource and Low-shot PET Foundation Model in Projection Domain](https://mlsanity.com/paper/2509.09130/) (2025) - [Iterative Diffusion-Refined Neural Attenuation Fields for Multi-Source Stationary CT Reconstruction: NAF Meets Diffusion Model](https://mlsanity.com/paper/2511.14310/) (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) - [TempDiffReg: Temporal Diffusion Model for Non-Rigid 2D-3D Vascular Registration](https://mlsanity.com/paper/2601.18168/) (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) - [Anatomy-preserving unpaired cone-beam CT refinement for image-guided radiotherapy using pseudo-label guided diffusion](https://mlsanity.com/paper/2610.06094/) (2026) ## Abstract (arXiv) During the process of computed tomography (CT), metallic implants often cause disruptive artifacts in the reconstructed images, impeding accurate diagnosis. Several supervised deep learning-based approaches have been proposed for reducing metal artifacts (MAR). However, these methods heavily rely on training with simulated data, as obtaining paired metal artifact CT and clean CT data in clinical settings is challenging. This limitation can lead to decreased performance when applying these methods in clinical practice. Existing unsupervised MAR methods, whether based on learning or not, typically operate within a single domain, either in the image domain or the sinogram domain. In this paper, we propose an unsupervised MAR method based on the diffusion model, a generative model with a high capacity to represent data distributions. Specifically, we first train a diffusion model using CT images without metal artifacts. Subsequently, we iteratively utilize the priors embedded within the pre-trained diffusion model in both the sinogram and image domains to restore the degraded portions caused by metal artifacts. This dual-domain processing empowers our approach to outperform existing unsupervised MAR methods, including another MAR method based on the diffusion model, which we have qualitatively and quantitatively validated using synthetic datasets. Moreover, our method demonstrates superior visual results compared to both supervised and unsupervised methods on clinical datasets.