Simultaneous multi-color optical sectioning fluorescence microscopy with wavelength-coded volume holographic gratings

Chia, Yu-Hsin; Yeh, J. Andrew; Huang, Yi‐You; Luo, Yuan

doi:10.1364/oe.409179

Cited by 10 publications

(2 citation statements)

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“…Recently a multiplane qualitative DPC imaging technique using volume holographic grating was proposed [32]. Simultaneous multiplane DPC images were acquired by the use of an angularly multiplexed volume holographic grating [33,34]. To get the asymmetric illumination required for the DPC a thin film transistor (TFT) panel has been employed in regular Kohler illumination for dynamic control of illumination.…”

Section: Qualitative Differential Phase Contrast Microscopymentioning

confidence: 99%

Isotropic quantitative differential phase contrast imaging techniques: a review

Vyas¹,

Li²,

Lin³

et al. 2022

J. Phys. D: Appl. Phys.

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Optical phase shifts generated by the spatial variation of refractive index and thickness inside the transparent samples can be determined by intensity measurements through quantitative phase contrast imaging. In this review, we focus on isotropic quantitative differential phase-contrast microscopy(qDPC), which is a non-interferometric quantitative phase imaging technique and belongs to the class of deterministic phase retrieval from intensity. The qDPC is based on the principle of a weak object transfer function together with the first-order Born approximation in a partially coherent illumination system and wide-field detection, which offers multiple advantages. We review basic principles, imaging systems, and demonstrate examples of differential phase contrast (DPC) imaging for biomedical applications. In addition to the previous work, we present the latest results for isotropic phase contrast enhancements using a deep learning approach. We implemented a supervised learning approach with the U-Net model to reduce the number of measurements required for multi-axis measurements associated with the isotropic phase transfer function. We show that a well-designed and trained neural network provide a fast and efficient way to predict quantitative phase maps for live cells, which can help in determining morphological parameters. The prospects of deep learning in quantitative phase microscopy, particularly for isotropic quantitative phase estimation, are discussed.

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Section: Qualitative Differential Phase Contrast Microscopymentioning

confidence: 99%

Isotropic quantitative differential phase contrast imaging techniques: a review

Vyas¹,

Li²,

Lin³

et al. 2022

J. Phys. D: Appl. Phys.

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“…Furthermore, the utilization of speckle illumination in the HiLo approach can facilitate deep tissue penetration. [ 18 , 19 , 20 ] Even though HiLo endo‐microscopy renders remarkable optical sectioning images, it necessitates axial scanning by moving the endoscope probe or sample, thereby greatly increasing the risk of injuring tissue from external compression. The optically sectioned HiLo images need to be additionally computed from pairwise images.…”

Section: Introductionmentioning

confidence: 99%

In Vivo Intelligent Fluorescence Endo‐Microscopy by Varifocal Meta‐Device and Deep Learning

Chia,

Liao,

Vyas

et al. 2024

Advanced Science

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Endo‐microscopy is crucial for real‐time 3D visualization of internal tissues and subcellular structures. Conventional methods rely on axial movement of optical components for precise focus adjustment, limiting miniaturization and complicating procedures. Meta‐device, composed of artificial nanostructures, is an emerging optical flat device that can freely manipulate the phase and amplitude of light. Here, an intelligent fluorescence endo‐microscope is developed based on varifocal meta‐lens and deep learning (DL). The breakthrough enables in vivo 3D imaging of mouse brains, where varifocal meta‐lens focal length adjusts through relative rotation angle. The system offers key advantages such as invariant magnification, a large field‐of‐view, and optical sectioning at a maximum focal length tuning range of ≈2 mm with 3 µm lateral resolution. Using a DL network, image acquisition time and system complexity are significantly reduced, and in vivo high‐resolution brain images of detailed vessels and surrounding perivascular space are clearly observed within 0.1 s (≈50 times faster). The approach will benefit various surgical procedures, such as gastrointestinal biopsies, neural imaging, brain surgery, etc.

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