Ultra-low-cost and high-fidelity NIR-II confocal laser scanning microscope with Bessel beam excitation and SiPM detection DOI Creative Commons
Xueli Chen,

Xinyu Wanng,

Tianyu Yan

et al.

Published: May 23, 2024

The NIR-II based CLSM has problems such as expensive detector and reduced image resolution. Here, by simultaneously using a low-cost silicon photomultiplier (SiPM) Bessel beam an excitation, we developed ultra-low-cost high-fidelity confocal laser scanning microscope. introduction of compensates to some extent for the weakening spatial resolution caused increase in wavelength light NIR region. use SiPM reduces cost fluorescence detection module CLSM, while enabling ultra-broadband signals spanning visible regions.

Language: Английский

Deep-LASI, single-molecule data analysis software DOI
Pooyeh Asadiatouei,

Clemens-Bässem Salem,

Simon Wanninger

et al.

Biophysical Journal, Journal Year: 2024, Volume and Issue: 123(17), P. 2682 - 2695

Published: Feb. 20, 2024

Language: Английский

Citations

2
Providing Real-World Benchmarks for Super-Resolving Fluorescence Microscope Imagery Using Generative Adversarial Networks DOI
D. James Cooper, Tahir Bachar Issa, Claudio Vinegoni

et al.

Published: June 25, 2024

Language: Английский

Citations

2
Visualizing DNA/RNA, Proteins, and Small Molecule Metabolites within Live Cells DOI
Dongling Jia,

Minhui Cui,

Xianting Ding

et al.

Small, Journal Year: 2024, Volume and Issue: 20(46)

Published: Aug. 3, 2024

Live cell imaging is essential for obtaining spatial and temporal insights into dynamic molecular events within heterogeneous individual cells, in situ intracellular networks, vivo organisms. Molecular tracking live cells also a critical general requirement studying physiological processes biology, cancer, developmental neuroscience. Alongside this context, review provides comprehensive overview of recent research progress live-cell RNAs, DNAs, proteins, small-molecule metabolites, as well their applications diagnosis, immunodiagnosis, biochemical diagnosis. A series advanced techniques have been introduced summarized, including high-precision imaging, high-resolution low-abundance multidimensional multipath rapid computationally driven methods, all which offer valuable disease prevention, treatment. This article addresses the current challenges, potential solutions, future development prospects field.

Language: Английский

Citations

2
Sound Out the Deep Clarity: Super-resolution Photoacoustic Imaging at Depths DOI
Nanchao Wang, Junjie Yao

IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control, Journal Year: 2024, Volume and Issue: 71(12: Breaking the Resolution), P. 1801 - 1813

Published: Sept. 2, 2024

Photoacoustic imaging (PAI), also known as optoacoustic imaging, is a hybrid modality that combines the rich contrast of optical with deep penetration ultrasound imaging. Over past decade, PAI has been increasingly utilized in biomedical studies, providing high-resolution high-contrast images endogenous and exogenous chromophores various fundamental clinical research. However, faces challenges achieving high resolution tissue simultaneously, limited by acoustic interactions tissues. Overcoming these limitations crucial for maximizing potential applications. Recent advances super-resolution have opened new possibilities at greater depths. This review provides comprehensive summary promising strategies, highlights their representative applications, envisions future directions, discusses broader impact on

Language: Английский

Citations

2
Correction: Enhancing image resolution of confocal fluorescence microscopy with deep learning DOI Creative Commons

Boyi Huang,

Jia Li, Bowen Yao

et al.

PhotoniX, Journal Year: 2023, Volume and Issue: 4(1)

Published: Jan. 19, 2023

Language: Английский

Citations

5
Small training dataset convolutional neural networks for application-specific super-resolution microscopy DOI Creative Commons
Varun Mannam, Scott S. Howard

Journal of Biomedical Optics, Journal Year: 2023, Volume and Issue: 28(03)

Published: March 14, 2023

SignificanceMachine learning (ML) models based on deep convolutional neural networks have been used to significantly increase microscopy resolution, speed [signal-to-noise ratio (SNR)], and data interpretation. The bottleneck in developing effective ML systems is often the need acquire large datasets train network. We demonstrate how adding a "dense encoder-decoder" (DenseED) block can be effectively network that produces super-resolution (SR) images from conventional diffraction-limited (DL) trained using small dataset [15 fields of view (FOVs)].AimThe helps retrieve SR information DL image when with massive training dataset. aim this work estimates modifications enable dataset.ApproachWe employ "DenseED" blocks existing architectures. DenseED use dense layer concatenates features previous next layer. fully (FCNs) estimate (15 FOVs) human cells Widefield2SIM fluorescent-labeled fixed bovine pulmonary artery endothelial samples.ResultsConventional without fail accurately while including can. average peak SNR (PSNR) resolution improvements achieved by containing are ≈3.2 dB 2 × , respectively. evaluated various configurations target generation methods (e.g., experimentally captured computationally generated target) FCNs showed simple outperforms compared blocks.ConclusionsDenseED show accurate extraction even if model 15 FOVs. This approach shows applications smaller application-specific imaging platforms there promise for applying other modalities, such as MRI/x-ray, etc.

Language: Английский

Citations

4
深度学习在超分辨显微成像中的研究进展（特邀） DOI

鲁心怡 Lu Xinyi,

黄昱 Huang Yu,

张梓童 Zhang Zitong

et al.

Laser & Optoelectronics Progress, Journal Year: 2024, Volume and Issue: 61(16), P. 1611002 - 1611002

Published: Jan. 1, 2024

Citations

1
Real-Time Resolution Enhancement of Confocal Laser Scanning Microscopy via Deep Learning DOI Creative Commons

Zhiying Cui,

Yi Xing, Yunbo Chen

et al.

Photonics, Journal Year: 2024, Volume and Issue: 11(10), P. 983 - 983

Published: Oct. 19, 2024

Confocal laser scanning microscopy is one of the most widely used tools for high-resolution imaging biological cells. However, resolution conventional confocal technology limited by diffraction, and more complex optical principles expensive optical-mechanical structures are usually required to improve resolution. This study proposed a deep residual neural network algorithm that can effectively in real time. The reliability real-time performance were verified through experiments on different structures, an less than 120 nm was achieved cost-effective manner. contributes improvement expands application scenarios imaging.

Language: Английский

Citations

1
Universal and High-Fidelity Resolution Extending for Fluorescence Microscopy Using a Single-Training Physics-Informed Sparse Neural Network DOI Creative Commons

Zitong Ye,

Yuran Huang, Jinfeng Zhang

et al.

Intelligent Computing, Journal Year: 2024, Volume and Issue: 3

Published: Jan. 1, 2024

As a supplement to optical super-resolution microscopy techniques, computational methods have demonstrated remarkable results in alleviating the spatiotemporal imaging trade-off. However, they commonly suffer from low structural fidelity and universality. Therefore, we herein propose deep-physics-informed sparsity framework designed holistically synergize strengths of physical models (image blurring processes), prior knowledge (continuity constraints), back-end optimization algorithm deblurring), deep learning (an unsupervised neural network). Owing utilization multipronged strategy, trained network can be applied variety modalities samples enhance resolution by factor at least 1.67 without requiring additional training or parameter tuning. Given advantages high accessibility universality, proposed method will considerably existing techniques wide range applications biomedical research.

Language: Английский

Citations

0
植入式荧光内窥显微技术及其在活体脑成像中的应用（特邀） DOI Open Access

林方睿 Lin Fangrui,

张晨爽 Zhang Chenshuang,

连晓倩 Lian Xiaoqian

et al.

Chinese Journal of Lasers, Journal Year: 2024, Volume and Issue: 51(1), P. 0107001 - 0107001

Published: Jan. 1, 2024

高时空分辨可视化技术是脑科学研究的重要工具。荧光显微成像技术在特异性、多样性、图像对比度和时空分辨率等方面具有显著优势,但由于光在组织中的穿透深度有限,无创的荧光成像难以在活体水平获取深层脑区神经血管单元的高分辨结构和功能信息。因此,在脑科学研究中,荧光内窥显微成像技术受到越来越多研究者的青睐。得益于相关科学技术的发展,内窥镜探头在保持高性能的同时,实现了小型化并提供了更大的灵活性,可以植入活体大脑的不同深度处,开展特定深层脑区的功能调控研究。本综述介绍了基于梯度折射率透镜和单根多模光纤这两种探头的植入式荧光内窥显微成像技术及其发展和迭代进程,概述了它们在高分辨活体脑成像研究中的应用,以及在临床神经外科手术中的初步探索性应用。最后,展望了荧光内窥脑成像技术未来的发展前景。

Citations

0