MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography

• URL: http://arxiv.org/abs/2603.13447v1
• Date: Fri, 13 Mar 2026 13:41:23 GMT
• Title: MGMAR: Metal-Guided Metal Artifact Reduction for X-ray Computed Tomography
• Abstract summary: metallic implants violate standard CT forward-model assumptions, producing severe streaking and shadowing artifacts.<n>We propose MGMAR, a metal-guided MAR method that explicitly leverages metal-related information throughout the reconstruction pipeline.<n>MGMAR achieves state-of-the-art performance, attaining an average final score of 0.89 on 29 clinical test cases.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An X-ray computed tomography (CT), metal artifact reduction (MAR) remains a major challenge because metallic implants violate standard CT forward-model assumptions, producing severe streaking and shadowing artifacts that degrade diagnostic quality. We propose MGMAR, a metal-guided MAR method that explicitly leverages metal-related information throughout the reconstruction pipeline. MGMAR first generates a high-quality prior image by training a conditioned implicit neural representation (INR) using metal-unaffected projections, and then incorporates this prior into a normalized MAR (NMAR) framework for projection completion. To improve robustness under severe metal corruption, we pretrain the encoder-conditioned INR on paired metal-corrupted and artifact-free CT images, thereby embedding data-driven prior knowledge into the INR parameter space. This prior-embedded initialization reduces sensitivity to random initialization and accelerates convergence during measurement-specific refinement. The encoder takes a metal-corrupted reconstruction together with a recursively constructed metal artifact image, enabling the latent field to capture metal-dependent global artifact patterns. After projection completion using the INR prior, we further suppress residual artifacts using a metal-conditioned correction network, where the metal mask modulates intermediate features via adaptive instance normalization to target metal-dependent secondary artifacts while preserving anatomical structures. Experiments on the public AAPM-MAR benchmark demonstrate that MGMAR achieves state-of-the-art performance, attaining an average final score of 0.89 on 29 clinical test cases.

Related papers

• MASC: Metal-Aware Sampling and Correction via Reinforcement Learning for Accelerated MRI [18.47534199434918]
  Metal implants in MRI cause severe artifacts that degrade image quality and hinder clinical diagnosis.<n>We propose MASC, a unified reinforcement learning framework that jointly optimize metal-aware k-space sampling and artifact correction for accelerated MRI.
  arXiv  Detail & Related papers  (2026-01-30T21:53:43Z)
• Direct Dual-Energy CT Material Decomposition using Model-based Denoising Diffusion Model [105.95160543743984]
  We propose a deep learning procedure called Dual-Energy Decomposition Model-based Diffusion (DEcomp-MoD) for quantitative material decomposition.<n>We show that DEcomp-MoD outperform state-of-the-art unsupervised score-based model and supervised deep learning networks.
  arXiv  Detail & Related papers  (2025-07-24T01:00:06Z)
• Reducing CT Metal Artifacts by Learning Latent Space Alignment with Gemstone Spectral Imaging Data [45.94938820436709]
  Metal artifacts in CT slices have long posed challenges in medical diagnostics.<n>We introduce the Latent Gemstone Spectral Imaging (GSI) Alignment Framework.<n>Our work is based on a key finding that even artifact-affected ordinary CT sequences contain sufficient information to discern detailed structures.
  arXiv  Detail & Related papers  (2025-03-27T08:35:10Z)
• ContextMRI: Enhancing Compressed Sensing MRI through Metadata Conditioning [51.26601171361753]
  We propose ContextMRI, a text-conditioned diffusion model for MRI that integrates granular metadata into the reconstruction process.<n>We show that increasing the fidelity of metadata, ranging from slice location and contrast to patient age, sex, and pathology, systematically boosts reconstruction performance.
  arXiv  Detail & Related papers  (2025-01-08T05:15:43Z)
• Unlocking the Potential of Early Epochs: Uncertainty-aware CT Metal Artifact Reduction [7.4965536794782945]
  In computed tomography (CT), the presence of metallic implants in patients often leads to disruptive artifacts in the reconstructed images. In this paper, we have discovered that the uncertainty image computed from the restoration result of initial training weights can effectively highlight high-frequency regions. We propose an uncertainty constraint (UC) loss that utilizes the uncertainty image as an adaptive weight to guide the MAR network to focus on the metal artifact region.
  arXiv  Detail & Related papers  (2024-06-18T01:36:58Z)
• MARformer: An Efficient Metal Artifact Reduction Transformer for Dental CBCT Images [53.62335292022492]
  Metal teeth implants could bring annoying metal artifacts during the CBCT imaging process. We develop an efficient Transformer to perform metal artifacts reduction (MAR) from dental CBCT images. A Patch-wise Perceptive Feed Forward Network (P2FFN) is also proposed to perceive local image information for fine-grained restoration.
  arXiv  Detail & Related papers  (2023-11-16T06:02:03Z)
• Unsupervised Polychromatic Neural Representation for CT Metal Artifact Reduction [48.1445005916672]
  We present a novel Polychromatic neural representation (Polyner) to tackle the challenging problem of CT imaging when metallic implants exist within the human body. Our Polyner achieves comparable or better performance than supervised methods on in-domain datasets.
  arXiv  Detail & Related papers  (2023-06-27T04:50:58Z)
• Orientation-Shared Convolution Representation for CT Metal Artifact Learning [63.67718355820655]
  During X-ray computed tomography (CT) scanning, metallic implants carrying with patients often lead to adverse artifacts. Existing deep-learning-based methods have gained promising reconstruction performance. We propose an orientation-shared convolution representation strategy to adapt the physical prior structures of artifacts.
  arXiv  Detail & Related papers  (2022-12-26T13:56:12Z)
• Negligible effect of brain MRI data preprocessing for tumor segmentation [36.89606202543839]
  We conduct experiments on three publicly available datasets and evaluate the effect of different preprocessing steps in deep neural networks. Our results demonstrate that most popular standardization steps add no value to the network performance. We suggest that image intensity normalization approaches do not contribute to model accuracy because of the reduction of signal variance with image standardization.
  arXiv  Detail & Related papers  (2022-04-11T17:29:36Z)
• Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning [30.977044473457]
  We present a novel deep-learning-based approach for metal artifact reduction (MAR) We train a neural network to restore the metal trace region values in the given metal-free sinogram. We then design a novel FBP reconstruction loss to encourage the network to generate more perfect completion results.
  arXiv  Detail & Related papers  (2021-09-28T04:40:57Z)
• Encoding Metal Mask Projection for Metal Artifact Reduction in Computed Tomography [21.5885187197634]
  Metal artifact reduction (MAR) in computed tomography (CT) is a notoriously challenging task because the artifacts are structured and non-local in the image domain. We propose to address the problem by (1) retaining the metal-affected regions in sinogram and (2) replacing the binarized metal trace with the metal mask projection. Our novel network yields more precise artifact-reduced images than the state-of-the-art approaches, especially when metallic objects are large.
  arXiv  Detail & Related papers  (2020-01-02T06:39:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.