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    A new super-resolution algorithm enhances optical-resolution photoacoustic microscopy (OR-PAM) image resolution by 1.7x. This method improves clarity for mouse retinal imaging without needing higher numerical aperture (NA).

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    Area of Science:

    • Biomedical Imaging
    • Optical Microscopy
    • Image Reconstruction

    Background:

    • Lateral resolution in optical-resolution photoacoustic microscopy (OR-PAM) is limited by probe beam focusing diameter.
    • Increasing numerical aperture (NA) improves resolution but reduces working distance and increases sensitivity to optical imperfections.
    • Current deconvolution algorithms for OR-PAM are constrained by signal-to-noise ratio limitations.

    Purpose of the Study:

    • To develop a novel super-resolution reconstruction algorithm for OR-PAM images.
    • To enhance image resolution without increasing the numerical aperture (NA) of the imaging system.
    • To improve the clinical research potential of OR-PAM.

    Main Methods:

    • A super-resolution reconstruction algorithm combining sparsity and deconvolution was proposed.
    • OR-PAM images were sparsely reconstructed using a constructed loss function that leverages image sparsity.
    • The gradient accelerated Landweber iterative algorithm was employed for deconvolution to achieve high-resolution images.

    Main Results:

    • The proposed algorithm improved the resolution of mouse retinal images by approximately 1.7 times.
    • Resolution enhancement was achieved without increasing the NA of the OR-PAM system.
    • The algorithm demonstrated superior performance over the Richardson-Lucy algorithm in terms of resolution and image quality.

    Conclusions:

    • The developed sparsity and deconvolution-based algorithm effectively enhances OR-PAM image resolution.
    • This method offers a viable approach for improving OR-PAM without optical system modifications.
    • The enhanced imaging quality provides a foundation for advancing OR-PAM in clinical research.