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Deep learning-based metal artifact reduction in CT for total knee arthroplasty.

Jimin Lee1,2,3, Hee-Dong Chae4,5, Hyungjoo Cho1

  • 1Program in Biomedical Radiation Sciences, Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, 1 Gwanak-ro, Gwanak-gu, Seoul, 08826, Republic of Korea.

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|November 12, 2025
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Summary

A new deep learning technique, KMAR-Net, significantly improves metal artifact reduction (MAR) in CT scans after total knee arthroplasty (TKA). This AI-powered method enhances image quality for better postoperative evaluation.

Keywords:
Artificial intelligenceCT artifactDeep learningMetal artifact reductionTotal knee arthroplasty

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Metal artifacts in CT scans pose challenges for evaluating patients with total knee arthroplasty (TKA).
  • Existing metal artifact reduction (MAR) methods have limitations in clinical practice.

Purpose of the Study:

  • To develop and evaluate a deep learning (DL)-based MAR technique, KMAR-Net, specifically for postoperative TKA CT scans.
  • To compare the performance of KMAR-Net against conventional MAR methods.

Main Methods:

  • Developed KMAR-Net using simulated CT images generated via sinogram handling.
  • Quantitative analysis involved measuring artifact area, mean attenuation, and standard deviation.
  • Qualitative analysis used visual grading for artifact severity, bone conspicuity, and soft tissue visualization.
  • A phantom study validated KMAR-Net's performance under controlled conditions.

Main Results:

  • KMAR-Net demonstrated superior reduction in artifact area, mean attenuation, and standard deviation compared to the projection-completion method.
  • Qualitative analysis showed KMAR-Net outperformed O-MAR in overall artifact reduction and soft tissue evaluation.
  • One reader found KMAR-Net superior for bone conspicuity (P=0.080), while the second reader strongly agreed (P<0.001).

Conclusions:

  • Deep learning-based KMAR-Net offers significant advantages for metal artifact reduction in postoperative TKA CT imaging.
  • KMAR-Net provides superior image quality compared to conventional MAR techniques, aiding clinical assessment.