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Multifunctional GAN-based optimization for X-ray tomography under different conditions.

Yu Guan, Shou Zhang, Hongwei Wang

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    This study introduces a generative adversarial network (GAN) protocol for X-ray tomography, effectively correcting artifacts, reducing noise, and enhancing image resolution for biological samples.

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    Area of Science:

    • Medical Imaging
    • Computational Imaging
    • Image Processing

    Background:

    • X-ray tomography often suffers from artifacts like ring artifacts and noise.
    • Achieving high spatial resolution and contrast-to-noise ratio simultaneously is challenging.
    • Existing deep learning and conventional methods have limitations in speed and information loss.

    Purpose of the Study:

    • To develop a multifunctional X-ray tomographic protocol using generative adversarial networks (GANs).
    • To simultaneously correct artifacts, suppress noise, and achieve super-resolution in reconstructed images.
    • To provide a faster and more efficient tool for high-resolution X-ray tomography.

    Main Methods:

    • A data preprocessing module was developed.
    • A multifunctional GAN-based loss function was designed to address ring artifacts and super-resolution.
    • The protocol was tested on X-ray cone-beam computed tomography scans of biological samples.

    Main Results:

    • The protocol successfully removed ring artifacts.
    • It improved the contrast-to-noise ratio (CNR) and spatial resolution (SR) of reconstructed images.
    • Adaptive correction of various ring artifact types and intensities was demonstrated.

    Conclusions:

    • The developed GAN protocol offers a robust solution for artifact correction, noise suppression, and super-resolution in X-ray tomography.
    • It achieves higher processing speeds and minimal information loss compared to existing methods.
    • This provides an optimization tool for realizing large fields of view and high-resolution X-ray imaging.