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    This study presents a fast machine learning adaptive optics method to restore doughnut-shaped beams in scattering media. The technique achieves high accuracy, enabling advanced bioimaging applications.

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    Area of Science:

    • Optics
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Doughnut-shaped beams are crucial for super-resolution microscopy, lithography, and laser trapping.
    • Wavefront aberrations in scattering media challenge the maintenance of doughnut-shaped foci.
    • Adaptive optics are essential for correcting these aberrations.

    Purpose of the Study:

    • To develop a high-speed, machine learning-based adaptive optics method for recovering doughnut-shaped foci in scattering media.
    • To establish a relationship between distorted point spread functions and Zernike mode coefficients for phase correction.
    • To demonstrate the method's applicability in scanning microscopy and deep tissue bioimaging.

    Main Methods:

    • A machine learning model was trained to predict phase correction coefficients based on distorted doughnut-shaped intensity point spread functions.
    • The model utilized the first 15 Zernike modes for phase correction.
    • The system incorporated 101,784 optical control elements for wavefront manipulation.

    Main Results:

    • Wavefront aberration prediction was achieved within approximately 17 milliseconds on a personal computer.
    • A correction accuracy of 97.5% was consistently obtained over 200 repeated tests.
    • The method was theoretically validated for scanning microscopy applications.

    Conclusions:

    • The developed machine learning adaptive optics method offers high-speed and accurate recovery of doughnut-shaped foci in scattering media.
    • The method's efficiency and scalability suggest significant potential for advanced bioimaging, including deep tissue stimulated emission depletion (STED) microscopy.
    • This approach could overcome current limitations in biological imaging through scattering tissues.