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    A new wavelet-inspired network, WOADNet, effectively reduces metal artifacts in computed tomography (CT) scans. This interpretable framework significantly improves image quality by leveraging sparse coding and adaptive dictionary learning for better artifact suppression.

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    Area of Science:

    • Medical Imaging
    • Image Processing
    • Artificial Intelligence in Radiology

    Background:

    • Metal artifacts are a significant challenge in computed tomography (CT) imaging, degrading image quality and hindering diagnosis.
    • Existing metal artifact reduction (MAR) techniques often lack interpretability, fail to fully utilize prior knowledge of artifacts, or struggle with complex image textures.

    Purpose of the Study:

    • To introduce a novel and interpretable framework, the wavelet-inspired oriented adaptive dictionary network (WOADNet), for enhanced metal artifact reduction in CT.
    • To address limitations of current MAR methods by improving artifact feature extraction and model interpretability.

    Main Methods:

    • WOADNet utilizes sparse coding with orientational information in the wavelet domain, incorporating multiangle rotations for high-precision filter parameterization.
    • A reweighted sparse constraint framework is integrated into convolutional dictionary learning.
    • A cross-space, multiscale attention mechanism constructs an adaptive convolutional dictionary unit for artifact feature encoding, enabling flexible weight adjustment.

    Main Results:

    • WOADNet demonstrated superior performance in suppressing metal artifacts compared to traditional and state-of-the-art MAR methods.
    • Experimental results on synthetic and clinical datasets confirmed significant improvements in CT image quality.

    Conclusions:

    • The proposed WOADNet framework offers an effective and interpretable solution for metal artifact reduction in CT imaging.
    • The network's ability to leverage sparse coding, adaptive dictionaries, and attention mechanisms leads to substantial enhancements in image quality.