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    Area of Science:

    • Medical Imaging
    • Physics
    • Biomedical Engineering

    Background:

    • Material decomposition in X-ray imaging uses energy-dependent attenuation but often increases noise.
    • Propagation-based X-ray phase-contrast imaging reduces noise, especially for weakly attenuating objects.
    • Visualizing fine anatomical structures in lungs is challenging due to strong bone attenuation obscuring soft tissue signals.

    Purpose of the Study:

    • To combine spectral phase-contrast imaging with material decomposition for enhanced visualization and separation of weakly attenuating features.
    • To develop and validate an algorithm for simultaneous material decomposition and noise reduction in X-ray imaging.
    • To demonstrate the technique's efficacy in imaging biological samples, specifically a rabbit kitten's lung.

    Main Methods:

    • Developed a novel algorithm integrating spectral phase-contrast imaging and material decomposition.
    • Verified the algorithm using numerical simulations and experimental measurements with aluminum and poly(methyl methacrylate) samples.
    • Applied the technique to image a rabbit kitten's lung, focusing on separating bone from soft tissue.

    Main Results:

    • Achieved clean material decomposition without residual phase-contrast effects.
    • Generated an unobstructed image of the lung, effectively removing bone interference.
    • Demonstrated successful visualization of weakly attenuating features and separation from overlying structures.

    Conclusions:

    • Spectral phase-contrast imaging combined with material decomposition offers a powerful approach for visualizing weakly attenuating structures.
    • This technique can overcome limitations in current X-ray imaging, particularly for lung research.
    • The method shows potential for various applications where phase-contrast imaging and material decomposition are advantageous.