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Related Experiment Video

Updated: Jun 19, 2026

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences
08:19

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Published on: May 17, 2018

Adaptive thresholding for CT metal artifact reduction via LangGraph.

Yeonghyeon Kim1, Kyungsang Kim2, Dongheon Lee3,4,5

  • 1Interdisciplinary Program in Bioengineering, Graduate School, Seoul National University, Seoul, Republic of Korea.

Physics in Medicine and Biology
|June 17, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces LangGraph-MAR, a novel framework for metal artifact reduction (MAR) in computed tomography (CT) that uses adaptive thresholding and automated quality assessment to improve image clarity. The new method significantly enhances diagnostic reliability by effectively reducing artifacts from various metal implants.

Keywords:
LangGraphcomputed tomographymetal artifact reduction

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Image Processing

Background:

  • Metal artifacts in computed tomography (CT) significantly degrade image quality, obscuring anatomical details and posing diagnostic challenges.
  • Current metal artifact reduction (MAR) methods often struggle with varying metal object properties (material, size, location), necessitating optimized thresholding strategies.

Purpose of the Study:

  • To introduce LangGraph-MAR, a novel framework for CT metal artifact reduction.
  • To develop an adaptive threshold optimization integrated with automated image quality assessment for MAR.
  • To enhance the diagnostic reliability and clinical decision-making in CT imaging with metal implants.

Main Methods:

  • Utilized a node-based LangGraph architecture with specialized functions for reconstruction, sinogram inpainting, and L1-based metal soft-thresholding.
  • Implemented a ground checking node, a quality assessment classifier trained on artifact-corrupted and artifact-free images, for automated iterative MAR.
  • Achieved convergence to optimal threshold values through an automated, iterative process guided by image quality assessment.

Main Results:

  • LangGraph-MAR demonstrated superior performance compared to baseline MAR methods in CT.
  • Achieved significant Structural Similarity Index Measure (SSIM) improvements: 8.64% for body and 5.61% for head protocols against InDuDoNet+.
  • Showcased substantial recovery of structural information in artifact-affected regions through detailed case and region of interest analyses.

Conclusions:

  • The proposed LangGraph-MAR framework ensures consistent performance across diverse metal implants via adaptive thresholding.
  • The modular, plug-and-play design enhances diagnostic reliability, supporting more effective clinical decision-making.
  • The developed framework offers a robust solution for improving CT image quality in the presence of metal artifacts.