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Metal Artifacts Reducing Method Based on Diffusion Model Using Intraoral Optical Scanning Data for Dental Cone-Beam

Yuyang Wang, Xiaomo Liu, Liang Li

    IEEE Transactions on Medical Imaging
    |August 7, 2024
    PubMed
    Summary

    This study introduces a novel metal artifact reduction (MAR) method for dental cone-beam computed tomography (CBCT). By integrating intraoral optical scanning data with CBCT, it significantly improves image quality for better medical diagnosis.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Dental Technology

    Background:

    • Metal artifacts in dental cone-beam computed tomography (CBCT) degrade image quality.
    • These artifacts can compromise the accuracy of medical diagnoses.
    • Existing methods for metal artifact reduction (MAR) have limitations.

    Purpose of the Study:

    • To develop a novel MAR method for CBCT by integrating data from two modalities.
    • To reduce metal artifacts in the projection domain using a guided-diffusion model.
    • To enhance the accuracy of dental imaging and diagnosis.

    Main Methods:

    • Integration of CBCT data with intraoral optical scanning data.
    • Utilizing a guided-diffusion model for artifact correction in the projection domain.
    • Implementation of a multi-channel generation method within the diffusion model, considering CBCT's physical mechanisms.

    Main Results:

    • Demonstrated feasibility and efficacy of the proposed MAR approach.
    • Successful reduction of metal artifacts in CBCT images.
    • Improved image quality for enhanced diagnostic capabilities.

    Conclusions:

    • The novel integration of intraoral optical scanning data with CBCT data effectively reduces metal artifacts.
    • This approach represents the first use of intraoral optical scanning data in conjunction with diffusion models for projection domain artifact correction.
    • The modified diffusion model shows improved adaptation to the physical model of CBCT, offering a promising solution for artifact reduction.