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Related Experiment Video

Updated: Jul 20, 2025

Author Spotlight: Advancing CBCT and Digital Dental Image Integration with AI-Assisted Digitization
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A new dental CBCT metal artifact reduction method based on a dual-domain processing framework.

Hui Tang1,2, Yu Bing Lin1, Su Dong Jiang3

  • 1Laboratory of Image Science and Technology, School of Computer Science and Engineering, Southeast University, Nanjing, People's Republic of China.

Physics in Medicine and Biology
|July 31, 2023
PubMed
Summary

A new dual-domain method effectively reduces metal artifacts in dental cone beam computed tomography (CBCT) images using projection correction and convolutional neural networks (CNNs). This technique significantly improves image quality for dental diagnostics.

Keywords:
CBCTU-netmetal artifact reductionsinogram linearization correction

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Area of Science:

  • Medical Imaging
  • Radiology
  • Computer Vision

Background:

  • Cone beam computed tomography (CBCT) is crucial for dental diagnostics.
  • Metallic implants in patients cause severe artifacts in CBCT images, hindering diagnosis.
  • Existing artifact reduction methods struggle with the unique challenges of dental CBCT data.

Purpose of the Study:

  • To develop a novel method for reducing metal artifacts in dental CBCT images.
  • To improve the diagnostic quality of CBCT images with metallic dental implants.
  • To introduce a dual-domain processing framework combining projection correction and deep learning.

Main Methods:

  • A three-stage approach involving volume reconstruction, metal segmentation, and projection correction.
  • Utilized linear interpolation and prior-based beam hardening correction in the projection domain.
  • Employed two concatenated U-Net based convolutional neural network (CNN) models for image post-processing.

Main Results:

  • The proposed method significantly reduced metal artifacts in both simulated and clinical dental CBCT images.
  • Achieved superior performance compared to frequency domain fusion (FS-MAR) and state-of-the-art CNN methods.
  • Quantitatively evaluated using Normalized Root Mean Square Difference (NRMSD) and Structural Similarity Index (SSIM), yielding excellent results (NRMSD: 4.0196, SSIM: 0.9924).

Conclusions:

  • The developed dual-domain processing framework effectively addresses metal artifacts in dental CBCT.
  • The method offers a promising solution for enhancing diagnostic accuracy in implant dentistry.
  • This approach demonstrates the suitability of combining projection correction and CNNs for artifact reduction.