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Related Experiment Video

Updated: Jan 14, 2026

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
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Residual Metal Artifact Reduction in CT Images: An Unsupervised Residual and Contrastive Learning Approach for

YongSoo Kim1,2, Jung-Woo Lee3, Byung Chul Lee1,2,4

  • 1Division of Bio-Medical Science & Technology, KIST School, Korea National University of Science and Technology, Seoul, South Korea.

Medical Physics
|October 28, 2025
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Summary

This study introduces an unsupervised deep learning model for metal artifact reduction (MAR) in CT images. The novel approach effectively reduces artifacts without needing ground truth data, improving image quality.

Keywords:
CT Physicsmetal artifact reductionunsupervised learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence

Background:

  • Computed tomography (CT) images frequently contain metal artifacts from implants.
  • Metal artifacts degrade image quality due to x-ray beam hardening.
  • Traditional methods for metal artifact reduction (MAR) are often empirical.

Purpose of the Study:

  • To develop an effective MAR method using unsupervised deep learning.
  • To overcome the challenge of obtaining ground truth images for MAR in clinical settings.

Main Methods:

  • A two-stage unsupervised deep learning approach was proposed.
  • Stage 1 utilized a CT physics-inspired residual model for initial artifact extraction.
  • Stage 2 employed contrastive learning to further refine artifact reduction.

Main Results:

  • The proposed model demonstrated superior performance on three datasets.
  • It effectively reduced metal artifacts while preserving original body and metal structures.
  • Outperformed existing MAR models in artifact reduction and structure preservation.

Conclusions:

  • The unsupervised learning model offers a viable solution for MAR.
  • It addresses limitations associated with data construction in deep learning for MAR.
  • This method can advance achievements in the metal artifact reduction field.