Surpassing the optical diffraction limit by matrix structured illumination microscopy with patterned excitation and patterned stimulated emission depletion

2019 ◽  
Vol 439 ◽  
pp. 148-155
Author(s):  
Qingru Li ◽  
Chen Wei ◽  
Hua Huang ◽  
Xinyi Liu ◽  
Shurong Jiang ◽  
...  
Chemosensors ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 364
Author(s):  
Meiting Wang ◽  
Jiajie Chen ◽  
Lei Wang ◽  
Xiaomin Zheng ◽  
Jie Zhou ◽  
...  

The super-resolution imaging technique of structured illumination microscopy (SIM) enables the mixing of high-frequency information into the optical transmission domain via light-source modulation, thus breaking the optical diffraction limit. Correlative SIM, which combines other techniques with SIM, offers more versatility or higher imaging resolution than traditional SIM. In this review, we first briefly introduce the imaging mechanism and development trends of conventional SIM. Then, the principles and recent developments of correlative SIM techniques are reviewed. Finally, the future development directions of SIM and its correlative microscopies are presented.


2020 ◽  
Author(s):  
Nicholas Bender ◽  
Mengyuan Sun ◽  
Hasan Yılmaz ◽  
Joerg Bewersdorf ◽  
Hui Cao

Speckle patterns have been widely used in imaging techniques such as ghost imaging, dynamic speckle illumination microscopy, structured illumination microscopy, and photoacoustic fluctuation imaging. Recent advances in the ability to control the statistical properties of speckles has enabled the customization of speckle patterns for specific imaging applications. In this work, we design and create special speckle patterns for parallelized nonlinear pattern-illumination microscopy based on fluorescence photoswitching. We present a proof-of-principle experimental demonstration where we obtain a spatial resolution three times higher than the diffraction limit of the illumination optics in our setup. Furthermore, we show that tailored speckles vastly outperform standard speckles. Our work establishes that customized speckles are a potent tool in parallelized super-resolution microscopy.


Cells ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1760
Author(s):  
Joshua J. A. Poole ◽  
Leila B. Mostaço-Guidolin

Biological tissues are not uniquely composed of cells. A substantial part of their volume is extracellular space, which is primarily filled by an intricate network of macromolecules constituting the extracellular matrix (ECM). The ECM serves as the scaffolding for tissues and organs throughout the body, playing an essential role in their structural and functional integrity. Understanding the intimate interaction between the cells and their structural microenvironment is central to our understanding of the factors driving the formation of normal versus remodelled tissue, including the processes involved in chronic fibrotic diseases. The visualization of the ECM is a key factor to track such changes successfully. This review is focused on presenting several optical imaging microscopy modalities used to characterize different ECM components. In this review, we describe and provide examples of applications of a vast gamut of microscopy techniques, such as widefield fluorescence, total internal reflection fluorescence, laser scanning confocal microscopy, multipoint/slit confocal microscopy, two-photon excited fluorescence (TPEF), second and third harmonic generation (SHG, THG), coherent anti-Stokes Raman scattering (CARS), fluorescence lifetime imaging microscopy (FLIM), structured illumination microscopy (SIM), stimulated emission depletion microscopy (STED), ground-state depletion microscopy (GSD), and photoactivated localization microscopy (PALM/fPALM), as well as their main advantages, limitations.


2020 ◽  
Author(s):  
Jiji Chen ◽  
Hideki Sasaki ◽  
Hoyin Lai ◽  
Yijun Su ◽  
Jiamin Liu ◽  
...  

Abstract We demonstrate residual channel attention networks (RCAN) for restoring and enhancing volumetric time-lapse (4D) fluorescence microscopy data. First, we modify RCAN to handle image volumes, showing that our network enables denoising competitive with three other state-of-the-art neural networks. We use RCAN to restore noisy 4D super-resolution data, enabling image capture over tens of thousands of images (thousands of volumes) without apparent photobleaching. Second, using simulations we show that RCAN enables class-leading resolution enhancement, superior to other networks. Third, we exploit RCAN for denoising and resolution improvement in confocal microscopy, enabling ~2.5-fold lateral resolution enhancement using stimulated emission depletion (STED) microscopy ground truth. Fourth, we develop methods to improve spatial resolution in structured illumination microscopy using expansion microscopy ground truth, achieving improvements of ~1.4-fold laterally and ~3.4-fold axially. Finally, we characterize the limits of denoising and resolution enhancement, suggesting practical benchmarks for evaluating and further enhancing network performance.


Sign in / Sign up

Export Citation Format

Share Document