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    This study introduces a new method using Implicit Neural Representations (INR) to correct stripe artifacts in computed tomography (CT) sinogram data. The technique effectively removes ring artifacts, improving diagnostic image quality.

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    Area of Science:

    • Medical Imaging
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Stripe artifacts in sinogram data, caused by detector element inconsistencies, lead to ring artifacts in computed tomography (CT) images.
    • These artifacts severely degrade the diagnostic quality of CT images, necessitating effective correction methods.

    Purpose of the Study:

    • To present a novel method for correcting stripe artifacts in sinogram data using Implicit Neural Representations (INR).
    • To improve the clarity and diagnostic quality of reconstructed CT images by addressing artifacts in the projection domain.

    Main Methods:

    • The proposed method separates sinogram data into an Ideal Sinogram (IS) and Stripe Artifacts (SA), both parameterized by INR.
    • INR is utilized to correct defective pixel responses and learn angular stripe features.
    • An unsupervised iterative correction framework is employed within an optimization constraint.

    Main Results:

    • The method effectively separates and corrects stripe artifacts in the sinogram data.
    • Experimental results show significant improvement over state-of-the-art techniques in removing ring artifacts.
    • The proposed approach maintains the clarity and fidelity of the reconstructed CT images.

    Conclusions:

    • The novel INR-based method offers a powerful solution for stripe artifact correction in CT.
    • This technique enhances the diagnostic value of CT imaging by improving image quality.
    • The unsupervised, iterative approach provides effective artifact removal without compromising image integrity.