Ultra-low-cost and high-fidelity NIR-II confocal laser scanning microscope with Bessel beam excitation and SiPM detection DOI Creative Commons
Xinyu Wang, Tianyu Yan, Lin Wang

et al.

Biomedical Optics Express, Journal Year: 2024, Volume and Issue: 15(8), P. 4786 - 4786

Published: July 15, 2024

Confocal laser scanning microscopy (CLSM) is one of the most important imaging tools in biomedical field, and near-infrared-II (NIR-II, 900-1700nm) fluorescence technology has also made fruitful research progress deep recent years. The NIR-II based CLSM problems such as an expensive detector reduced image resolution caused by long wavelength excitation. Here, simultaneously using a low-cost silicon photomultiplier (SiPM) Bessel beam excitation, we developed ultra-low-cost high-fidelity confocal microscope. use SiPM reduces cost detection module CLSM, while enabling ultra-broadband signals spanning visible to regions. introduction compensates some extent for weakening spatial increase light NIR region. Experimental results show that can improve 12% when observing thin samples. With sample thickness, at wavelengths better than Gaussian NIR-I penetrable depth light. At deeper depths, superior same excitation power.

Language: Английский

A versatile Wavelet-Enhanced CNN-Transformer for improved fluorescence microscopy image restoration DOI
Qinghua Wang, Ziwei Li, Shuqi Zhang

et al.

Neural Networks, Journal Year: 2023, Volume and Issue: 170, P. 227 - 241

Published: Nov. 19, 2023

Language: Английский

Citations

18
Deep‐MSIM: Fast Image Reconstruction with Deep Learning in Multifocal Structured Illumination Microscopy DOI Creative Commons
Jianhui Liao, Chenshuang Zhang,

Xiangcong Xu

et al.

Advanced Science, Journal Year: 2023, Volume and Issue: 10(27)

Published: July 9, 2023

Abstract Fast and precise reconstruction algorithm is desired for multifocal structured illumination microscopy (MSIM) to obtain the super‐resolution image. This work proposes a deep convolutional neural network (CNN) learn direct mapping from raw MSIM images image, which takes advantage of computational advances learning accelerate reconstruction. The method validated on diverse biological structures in vivo imaging zebrafish at depth 100 µm. results show that high‐quality, can be reconstructed one‐third runtime consumed by conventional method, without compromising spatial resolution. Last but not least, fourfold reduction number required achieved using same architecture, yet with different training data.

Language: Английский

Citations

11
A rhodamine-based fluorescent probe for dynamic STED imaging of mitochondria DOI Creative Commons
Xinwei Gao, Songtao Cai, Luwei Wang

et al.

Biomedical Optics Express, Journal Year: 2024, Volume and Issue: 15(3), P. 1595 - 1595

Published: Jan. 29, 2024

Stimulated emission depletion (STED) microscopy holds tremendous potential and practical implications in the field of biomedicine. However, weak anti-bleaching performance remains a major challenge limiting application STED fluorescent probes. Meanwhile, main excitation wavelengths most reported probes were below 500 nm or above 600 nm, few them between 500-600 nm. Herein, we developed new tetraphenyl ethylene-functionalized rhodamine dye (TPERh) for mitochondrial dynamic cristae imaging that was rhodamine-based with an wavelength 560 The TPERh probe exhibits excellent properties low saturating stimulated radiation power super-resolution imaging. Given these outstanding properties, used to measure deformation, which has positive study mitochondria-related diseases.

Language: Английский

Citations

4
Multicolor single-molecule localization microscopy: review and prospect DOI Creative Commons

Xi Chen,

Xiangyu Wang,

Fang Huang

et al.

PhotoniX, Journal Year: 2024, Volume and Issue: 5(1)

Published: Oct. 2, 2024

Abstract Single-molecule localization microscopy (SMLM) surpasses the diffraction limit by randomly switching fluorophores between fluorescent and dark states, precisely pinpointing resulted isolated emission patterns, thereby reconstructing super-resolution images based on accumulated locations of thousands to millions single molecules. This technique achieves a ten-fold improvement in resolution, unveiling intricate details molecular activities structures cells tissues. Multicolor SMLM extends this capability imaging distinct protein species labeled with various probes, providing insights into structural intricacies spatial relationships among different targets. review explores recent advancements multicolor SMLM, evaluates strengths limitations each variant, discusses future prospects.

Language: Английский

Citations

4
Deep learning–enabled filter-free fluorescence microscope DOI Creative Commons
Bo Dai,

Shaojie You,

Kan Wang

et al.

Science Advances, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 1, 2025

Optical filtering is an indispensable part of fluorescence microscopy for selectively highlighting molecules labeled with a specific fluorophore and suppressing background noise. However, the utilization optical sets increases complexity, size, cost microscopic systems, making them less suitable multifluorescence channel, high-speed imaging. Here, we present filter-free imaging enabled deep learning–based digital spectral filtering. This approach allows automatic channel selection after image acquisition accurate prediction by computing color changes due to shifts presence excitation scattering. Fluorescence cells tissues various fluorophores was demonstrated under different magnification powers. The technique offers identification labeling robust sensitivity specificity, achieving consistent results reference standard. Beyond microscopy, learning–enabled strategy has potential drive development other biomedical applications, including cytometry endoscopy.

Language: Английский

Citations

0
A generative approach for lensless imaging in low-light conditions DOI Creative Commons
Ziyang Liu, Tianjiao Zeng, Xu Zhan

et al.

Optics Express, Journal Year: 2025, Volume and Issue: 33(2), P. 3021 - 3021

Published: Jan. 7, 2025

Lensless imaging offers a lightweight, compact alternative to traditional lens-based systems, ideal for exploration in space-constrained environments. However, the absence of focusing lens and limited lighting such environments often results low-light conditions, where measurements suffer from complex noise interference due insufficient capture photons. This study presents robust reconstruction method high-quality scenarios, employing two complementary perspectives: model-driven data-driven. First, we apply physics-model-driven perspective reconstruct range space pseudo-inverse measurement model—as first guidance extract information noisy measurements. Then, integrate generative-model-based suppress residual noises—as second noises initial results. Specifically, learnable Wiener filter-based module generates an initial, reconstruction. fast and, more importantly, stable generation clear image version, implement modified conditional generative diffusion module. converts raw into latent wavelet domain efficiency uses bidirectional training processes stabilization. Simulations real-world experiments demonstrate substantial improvements overall visual quality, advancing lensless challenging

Language: Английский

Citations

0
Dynamic Monitoring of Organelle Interactions in Living Cells via Two-Color Digitally Enhanced Stimulated Emission Depletion Super-resolution Microscopy DOI

Xiaochun Shen,

Luwei Wang,

Yong Guo

et al.

The Journal of Physical Chemistry Letters, Journal Year: 2025, Volume and Issue: 16(2), P. 596 - 603

Published: Jan. 8, 2025

One of the most significant advances in stimulated emission depletion (STED) super-resolution microscopy is its capacity for dynamic imaging living cells, including long-term tracking interactions between various cells or organelles. Consequently, multicolor STED plays a pivotal role biological research. Despite emergence numerous fluorescent probes characterized by low toxicity, high stability, brightness, and exceptional specificity, enabling with STED, practical implementation live-cell influenced several factors. These factors include power wavelength beam, duration imaging, size area, complexity sample preparation. Presently, major limitation requirement power, which hinders monitoring different organelles due to associated irreversible optical damage. To address this issue, paper emphasizes research findings based on digitally enhanced (DE-STED) technique. This method overcomes aforementioned challenge utilizing laser achieve prolonged two-color effectively mitigating phototoxic effects enhancing observe intracellular dynamics. With less than 1 mW, we achieved resolution about 87 nm, close that achievable conventional high-power technology.

Language: Английский

Citations

0
Accurate Classification of Time-varying Microalgae by Stokes Imaging with Multiple Polarization Illumination DOI

Byung-Won Han,

Jiajin Li,

Zheng Hu

et al.

IEEE Transactions on Instrumentation and Measurement, Journal Year: 2025, Volume and Issue: 74, P. 1 - 16

Published: Jan. 1, 2025

Language: Английский

Citations

0
Digital Redepleted of Stimulated Emission Depletion Microscopy for Noise Reduction and Resolution Improvement DOI
Xinwei Gao,

Yong Guo,

Luwei Wang

et al.

Analytical Chemistry, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

Stimulated emission depletion microscopy (STED) achieves resolution beyond the diffraction limit by employing a donut-shaped laser that selectively reduces fluorescence at periphery of excitation area. The imaging quality STED is closely tied to minimizing intermediate light from ring-depletion laser. In this study, we introduce method termed "digital redepleted STED," which uses frequency domain filtering generate an optimal donut profile subtracting "perfect donut" signal original data. This approach effectively background noise and enhances resolution. Through simulation experiments, demonstrate digitally doubled compatible with wide range biological samples can be adapted for two-organelle-structure 3D applications. We compare performance enhanced (De STED) deconvolution methods (STED Decon) in terms signal-to-background ratio (SBR) as evaluation metrics, find our SBR different compared origin STED. Our results indicate outperforms both De Decon complicated sample like mitochondria. anticipate will have broad applicability due its resolution, improved SBR, ease implementation.

Language: Английский

Citations

0
Distinct actin microfilament localization during early cell plate formation through deep learning-based image restoration DOI Creative Commons
Suzuka Kikuchi,

Tamio Kotaka,

Yuga Hanaki

et al.

Plant Cell Reports, Journal Year: 2025, Volume and Issue: 44(6)

Published: May 8, 2025

Language: Английский

Citations

0