temporal focusing
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2022 ◽  
Author(s):  
Yifan Wang ◽  
Yao Zheng ◽  
Yongxian Xu ◽  
Rongrong Li ◽  
Yameng Zheng ◽  
...  

Two-photon optogenetics enables selectively stimulating individual cells for manipulating neuronal ensembles. As the general photostimulation strategy, the patterned two-photon excitation has enabled millisecond-timescale activation for single or multiple neurons, but its activation efficiency is suffered from high laser power due to low beam-modulation efficiency. Here, we develop a high-efficiency beam-shaping method based on the Gerchberg-Saxton (GS) algorithm with spherical-distribution initial phase (GSSIP) to reduce the patterned two-photon excitation speckles and intensity. It can well control the phase of shaped beams to attain speckle-free accurate patterned illumination with an improvement of 44.21% in the modulation efficiency compared with that of the traditional GS algorithm. A combination of temporal focusing and the GSSIP algorithm (TF-GSSIP) achieves patterned focusing through 500-μm-thickness mouse brain slices, which is 2.5 times deeper than the penetration depth of TF-GS with the same signal-to-noise ratio (SNR). With our method, the laser power can be reduced to only 55.56% of that with traditional method (the temporal focusing with GS, TF-GS) to reliably evoke GCaMP6s response in C1V1-expressing cultured neurons with single-cell resolution. Besides, the photostimulation efficiency is remarkably increased by 80.19% at the same excitation density of 0.27 mW/μm2. This two-photon stimulation method with low-power, reliable and patterned illumination may pave the way for analyzing neural circuits and neural coding and decoding mechanism.



2022 ◽  
Author(s):  
Dominik Kühn ◽  
Alexander Treffer ◽  
Frank Wyrowski ◽  
Ruediger Grunwald


2021 ◽  
Author(s):  
Ruheng Shi ◽  
Yuanlong Zhang ◽  
Tiankuang Zhou ◽  
Lingjie Kong




Membranes ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 634
Author(s):  
Ruheng Shi ◽  
Yuanlong Zhang ◽  
Tiankuang Zhou ◽  
Lingjie Kong

High-speed, optical-sectioning imaging is highly desired in biomedical studies, as most bio-structures and bio-dynamics are in three-dimensions. Compared to point-scanning techniques, line scanning temporal focusing microscopy (LSTFM) is a promising method that can achieve high temporal resolution while maintaining a deep penetration depth. However, the contrast and axial confinement would still be deteriorated in scattering tissue imaging. Here, we propose a HiLo-based LSTFM, utilizing structured illumination to inhibit the fluorescence background and, thus, enhance the image contrast and axial confinement in deep imaging. We demonstrate the superiority of our method by performing volumetric imaging of neurons and dynamical imaging of microglia in mouse brains in vivo.



2021 ◽  
Vol 14 (8) ◽  
pp. 082008
Author(s):  
Kenta Inazawa ◽  
Keisuke Isobe ◽  
Tomohiro Ishikawa ◽  
Kana Namiki ◽  
Atsushi Miyawaki ◽  
...  


2021 ◽  
Author(s):  
Keisuke Isobe ◽  
Tomohiro Ishikawa ◽  
Kenta Inazawa ◽  
KANA NAMIKI ◽  
Atsushi Miyawaki ◽  
...  


2021 ◽  
Vol 7 (28) ◽  
pp. eaay5496
Author(s):  
Cheng Zheng ◽  
Jong Kang Park ◽  
Murat Yildirim ◽  
Josiah R. Boivin ◽  
Yi Xue ◽  
...  

Nonlinear optical microscopy has enabled in vivo deep tissue imaging on the millimeter scale. A key unmet challenge is its limited throughput especially compared to rapid wide-field modalities that are used ubiquitously in thin specimens. Wide-field imaging methods in tissue specimens have found successes in optically cleared tissues and at shallower depths, but the scattering of emission photons in thick turbid samples severely degrades image quality at the camera. To address this challenge, we introduce a novel technique called De-scattering with Excitation Patterning or “DEEP,” which uses patterned nonlinear excitation followed by computational imaging–assisted wide-field detection. Multiphoton temporal focusing allows high-resolution excitation patterns to be projected deep inside specimen at multiple scattering lengths due to the use of long wavelength light. Computational reconstruction allows high-resolution structural features to be reconstructed from tens to hundreds of DEEP images instead of millions of point-scanning measurements.



2021 ◽  
Author(s):  
Jialong Chen ◽  
Songyun GU ◽  
Yunlong Meng ◽  
Zhiqiang Fu ◽  
Shih-Chi CHEN


Author(s):  
Yuedong Li ◽  
Juan Song ◽  
Qinxiao Zhai ◽  
Weiyi Yin ◽  
Xinlan Tang ◽  
...  


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