Dataset Information

Real-time holographic lensless micro-endoscopy through flexible fibers via fiber bundle distal holography.

ABSTRACT: Fiber-based micro-endoscopes are a critically important tool for minimally-invasive deep-tissue imaging. However, current micro-endoscopes cannot perform three-dimensional imaging through dynamically-bent fibers without the use of bulky optical elements such as lenses and scanners at the distal end, increasing the footprint and tissue-damage. Great efforts have been invested in developing approaches that avoid distal bulky optical elements. However, the fundamental barrier of dynamic optical wavefront-distortions in propagation through flexible fibers limits current approaches to nearly-static or non-flexible fibers. Here, we present an approach that allows holographic, bend-insensitive, coherence-gated, micro-endoscopic imaging using commercially available multi-core fibers (MCFs). We achieve this by adding a partially-reflecting mirror to the distal fiber-tip, allowing to perform low-coherence full-field phase-shifting holography. We demonstrate widefield diffraction-limited reflection imaging of amplitude and phase targets through dynamically bent fibers at video-rate. Our approach holds potential for label-free investigations of dynamic samples.

SUBMITTER: Badt N

PROVIDER: S-EPMC9563069 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature

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Publications

Real-time holographic lensless micro-endoscopy through flexible fibers via fiber bundle distal holography.

Badt Noam N Katz Ori O

Nature communications 20221013 1

Fiber-based micro-endoscopes are a critically important tool for minimally-invasive deep-tissue imaging. However, current micro-endoscopes cannot perform three-dimensional imaging through dynamically-bent fibers without the use of bulky optical elements such as lenses and scanners at the distal end, increasing the footprint and tissue-damage. Great efforts have been invested in developing approaches that avoid distal bulky optical elements. However, the fundamental barrier of dynamic optical wav ...[more]

PMID: 36229450

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Project description:SignificanceLensless digital inline holographic microscopy (LDIHM) is an emerging quantitative phase imaging modality that uses advanced computational methods for phase retrieval from the interference pattern. The existing end-to-end deep networks require a large training dataset with sufficient diversity to achieve high-fidelity hologram reconstruction. To mitigate this data requirement problem, physics-aware deep networks integrate the physics of holography in the loss function to reconstruct complex objects without needing prior training. However, the data fidelity term measures the data consistency with a single low-resolution hologram without any external regularization, which results in a low performance on complex biological data.AimWe aim to mitigate the challenges with trained and physics-aware untrained deep networks separately and combine the benefits of both methods for high-resolution phase recovery from a single low-resolution hologram in LDIHM.ApproachWe propose a hybrid deep framework (HDPhysNet) using a plug-and-play method that blends the benefits of trained and untrained deep models for phase recovery in LDIHM. The high-resolution phase is generated by a pre-trained high-definition generative adversarial network (HDGAN) from a single low-resolution hologram. The generated phase is then plugged into the loss function of a physics-aware untrained deep network to regulate the complex object reconstruction process.ResultsSimulation results show that the SSIM of the proposed method is increased by 0.07 over the trained and 0.04 over the untrained deep networks. The average phase-SNR is elevated by 8.2 dB over trained deep models and 9.8 dB over untrained deep networks on the experimental biological cells (cervical cells and red blood cells).ConclusionsWe showed improved performance of the HDPhysNet against the unknown perturbation in the imaging parameters such as the propagation distance, the wavelength of the illuminating source, and the imaging sample compared with the trained network (HDGAN). LDIHM, combined with HDPhysNet, is a portable and technology-driven microscopy best suited for point-of-care cytology applications.

Dataset Information

Real-time holographic lensless micro-endoscopy through flexible fibers via fiber bundle distal holography.

Publications

Real-time holographic lensless micro-endoscopy through flexible fibers via fiber bundle distal holography.

Similar Datasets

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