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Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Iterative optical diffraction tomography with embedded regularization.

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    Total-variation regularization enhances optical diffraction tomography reconstructions. This method significantly improves signal-to-noise ratio (SNR) for highly scattering objects in numerical and experimental tests.

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    Area of Science:

    • Physics
    • Imaging Science
    • Applied Mathematics

    Background:

    • Optical diffraction tomography (ODT) is a powerful imaging technique.
    • Reconstruction of highly scattering objects in ODT remains challenging.
    • Iterative reconstruction methods are commonly used but can be sensitive to noise.

    Purpose of the Study:

    • To improve the signal-to-noise ratio (SNR) in optical diffraction tomography.
    • To apply total-variation regularization within an iterative framework for ODT.
    • To validate the effectiveness of the proposed method for highly scattering objects.

    Main Methods:

    • An iterative framework for optical diffraction tomography was developed.
    • Total-variation regularization was incorporated into each iteration of the framework.
    • Numerical simulations and experimental measurements were conducted using various highly scattering objects.

    Main Results:

    • Significant improvements in reconstruction SNR were demonstrated.
    • The method effectively handled highly scattering media.
    • Reconstructed images showed enhanced clarity and reduced artifacts.

    Conclusions:

    • Total-variation regularization is a valuable tool for enhancing ODT reconstructions.
    • The proposed iterative approach offers improved performance for challenging scattering scenarios.
    • This technique has potential for various applications requiring high-resolution imaging of scattering samples.