Metal Artifact Reduction

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Most implemented papers archive 2025-07-28

13 shown of 13 papers with code (48 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
  3 Aug 2019 2 repositories listed
  Current deep neural network based approaches to computed tomography (CT) metal artifact reduction (MAR) are supervised methods that rely on synthesized metal artifacts for training.
• Radiologist-in-the-Loop Self-Training for Generalizable CT Metal Artifact Reduction
  26 Jan 2025 1 repository listed
  To address these limitations, we propose a novel Radiologist-In-the-loop SElf-training framework for MAR, termed RISE-MAR, which can integrate radiologists' feedback into the semi-supervised learning process,…
• Unsupervised CT Metal Artifact Reduction by Plugging Diffusion Priors in Dual Domains
  31 Aug 2023 1 repository listed
  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.
• Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction
  27 Jun 2023 1 repository listed Syntology ran 3 of 7 samples · 4 unverified · 7 pointer-only (licence)
  In this work, we present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body.
• MEPNet: A Model-Driven Equivariant Proximal Network for Joint Sparse-View Reconstruction and Metal Artifact Reduction in CT Images
  25 Jun 2023 1 repository listed
  Sparse-view computed tomography (CT) has been adopted as an important technique for speeding up data acquisition and decreasing radiation dose.
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning
  26 Dec 2022 1 repository listed
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts in the captured CT images and then impair the clinical treatment.
• Quad-Net: Quad-domain Network for CT Metal Artifact Reduction
  24 Jul 2022 1 repository listed
  Here we extend the state-of-the-art dual-domain deep network approach into a quad-domain counterpart so that all the features in the sinogram, image, and their corresponding Fourier domains are synergized to eliminate…
• Adaptive Convolutional Dictionary Network for CT Metal Artifact Reduction
  16 May 2022 1 repository listed
  By unfolding every iterative substep of the proposed algorithm into a network module, we explicitly embed the prior structure into a deep network, \emph{i.
• InDuDoNet+: A Deep Unfolding Dual Domain Network for Metal Artifact Reduction in CT Images
  23 Dec 2021 1 repository listed
  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.
• InDuDoNet: An Interpretable Dual Domain Network for CT Metal Artifact Reduction
  11 Sep 2021 1 repository listed
  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…
• DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction
  16 Feb 2021 1 repository listed
  With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT.
• Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
  5 Jun 2019 1 repository listed
  Extensive experiments show that our method significantly outperforms the existing unsupervised models for image-to-image translation problems, and achieves comparable performance to existing supervised models on a…
• Fast Enhanced CT Metal Artifact Reduction using Data Domain Deep Learning
  9 Apr 2019 1 repository listed
  The subsequent complete projection data is then used with FBP to reconstruct image intended to be free of artifacts.

Syntology lines on 1 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.