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    Area of Science:

    • Medical Imaging
    • Radiology
    • Image Reconstruction

    Background:

    • Wide-angle digital breast tomosynthesis (DBT) acquisition is limited by X-ray source motion blur and inherent ripple artifacts.
    • These artifacts can obscure subtle lesions and reduce diagnostic accuracy.

    Purpose of the Study:

    • To develop a deblurring technique that accelerates wide-angle DBT by reducing motion blur and accounting for ripple artifacts.
    • To improve lesion visibility and preserve image texture in DBT.

    Main Methods:

    • A novel deblurring framework was developed, modeling the point-spread function (PSF) as a depth-dependent kernel.
    • The framework includes a High-Attenuation Artifact Reduction (HAR) module for ripple suppression and a Ripple Artifact-Considered Deblurring (RAD) module.
    • RAD utilizes a convolutional neural network (CNN) regularizer and analytical data-fitting to handle artifacts and restore sharpness.

    Main Results:

    • The proposed method successfully enhanced lesion visibility and preserved textures on both numerical and physical phantom data.
    • No significant ringing artifacts were introduced, and quantitative evaluation metrics showed promising results.
    • The technique effectively suppressed high-attenuation artifacts and source motion blur.

    Conclusions:

    • A ripple-aware deblurring pipeline enables faster wide-angle DBT by allowing higher X-ray tube speeds.
    • This approach offers a practical solution to shorten patient compression time and improve clinical throughput.
    • The method maintains perceptual fidelity while jointly addressing artifacts and motion blur.