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Related Experiment Video

Updated: Sep 11, 2025

Anatomically Inspired Three-dimensional Micro-tissue Engineered Neural Networks for Nervous System Reconstruction, Modulation, and Modeling
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Lensless fiber endomicroscopic phase imaging using a physical model-driven neural network.

Yuhang Tang, Bin Zhao, Xinyi Ye

    Optics Express
    |August 13, 2025
    PubMed
    Summary

    This study introduces ASNet, a novel training-free method for lensless fiber endomicroscopic phase imaging. It enables label-free imaging of biological samples, overcoming data limitations in deep learning approaches.

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    Area of Science:

    • Biomedical Optics
    • Computational Imaging
    • Microscopy

    Background:

    • Lensless fiber endomicroscopy using multi-core fibers (MCF) offers minimally invasive, label-free imaging.
    • Conventional deep learning methods require extensive training data, which is challenging to obtain in real-world scenarios.

    Purpose of the Study:

    • To develop a training-free phase imaging method for lensless fiber endoscopes.
    • To address the limitations of data-dependent deep learning in MCF-based imaging.

    Main Methods:

    • Proposed an angular spectrum method-enhanced untrained neural network (ASNet).
    • Integrated a physical model with multi-distance speckles supervision for a lensless fiber endoscope system.
    • ASNet is a training-free approach.

    Main Results:

    • Demonstrated feasibility through simulations and experiments.
    • Successfully resolved a USAF-1951 target with 4.38 µm resolution.
    • Achieved phase reconstruction of HeLa cells.

    Conclusions:

    • ASNet enhances robustness and adaptability for MCF-based phase imaging.
    • Presents a versatile phase retrieval technique for compact, flexible imaging systems.
    • Offers potential for advanced applications and clinical diagnostics.