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    Area of Science:

    • Biomedical Optics
    • Microscopy
    • Computational Imaging

    Background:

    • Quantitative oblique back-illumination microscopy (qOBM) enables label-free 3D phase imaging of thick biological samples.
    • Traditional qOBM necessitates multiple captures, limiting imaging speed and increasing system complexity.

    Purpose of the Study:

    • To introduce single-capture qOBM (SCqOBM) using deep learning for rapid phase recovery.
    • To demonstrate SCqOBM's accuracy and potential for in-vivo and high-speed imaging applications.

    Main Methods:

    • Development of a deep learning model for phase reconstruction from a single oblique back-illumination image.
    • Validation of SCqOBM against traditional four-capture qOBM using diverse biological specimens.
    • Application of SCqOBM for in-vivo imaging of blood flow and high-speed tomographic imaging.

    Main Results:

    • SCqOBM accurately reconstructs phase information from a single capture, matching traditional qOBM results.
    • Demonstrated non-invasive visualization of blood flow in mouse brain and human arm.
    • Achieved single-slice quantitative phase imaging at 2 kHz and volumetric refractive index tomography at 10 volumes/second.

    Conclusions:

    • SCqOBM significantly enhances imaging speed and simplifies hardware requirements for quantitative phase imaging.
    • The technique offers transformative advantages for dynamic, real-time biomedical research and clinical diagnostics.
    • SCqOBM facilitates high-resolution, label-free 3D imaging, opening new avenues for in-vivo tissue analysis and hematological assessments.