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    Area of Science:

    • Biomedical Imaging
    • Computational Imaging
    • Optical Physics

    Background:

    • Optical coherence tomography (OCT) provides high-resolution cross-sectional images.
    • Super-resolution (SR) reconstruction aims to enhance the resolution of OCT images.
    • Existing SR methods may struggle with OCT-specific degradations like defocus and speckle noise.

    Purpose of the Study:

    • To introduce a physics-informed diffusion model (PIDM) for SR reconstruction of OCT data.
    • To develop an optimization framework that integrates physical models of OCT image formation with diffusion models.
    • To improve the fidelity and diagnostic quality of OCT images.

    Main Methods:

    • Developed a PIDM integrating physical models for OCT defocus, speckle noise, and digital sampling.
    • Modeled image degradations as a serial process reversed by the PIDM.
    • Used an optimization framework to maximize the likelihood of observed OCT data given the SR reconstruction.
    • Incorporated analytical light-propagation and statistical speckle noise models.

    Main Results:

    • Successfully reconstructed SR OCT images from complex data acquired using a line-scan OCT (LS-OCT) system.
    • Demonstrated improved image sharpness and contrast compared to standalone diffusion model (DM) SR methods.
    • Validated the method on standard resolution targets, plant tissue, and in vivo human cornea data.

    Conclusions:

    • Physics-informed diffusion models offer a viable approach for high-fidelity SR reconstruction in OCT.
    • Integrating OCT physics with diffusion models enhances SR performance.
    • This advancement holds potential for improving cellular-resolution OCT imaging of ophthalmic tissues, crucial for accurate diagnosis.