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In Vivo Superresolution Imaging of Neuronal Structure in the Mouse Brain.

Authors :
Urban, Ben Ewell
Xiao, Lei
Chen, Siyu
Yang, Huili
Dong, Biqin
Kozorovitskiy, Yevgenia
Zhang, Hao F.
Source :
IEEE Transactions on Biomedical Engineering. Jan2018, Vol. 65 Issue 1, p232-238. 7p.
Publication Year :
2018

Abstract

Objective: this study proposes and evaluates a technique for in vivo deep-tissue superresolution imaging in the light-scattering mouse brain at up to a 3.5 Hz 2-D imaging rate with a 21×21 μm2 field of view. Methods: we combine the deep-tissue penetration and high imaging speed of resonant laser scanning two-photon (2P) microscopy with the superresolution ability of patterned excitation microscopy. Using high-frequency intensity modulation of the scanned two-photon excitation beam, we generate patterned illumination at the imaging plane. Using the principles of structured illumination, the high-frequency components in the collected images are then used to reconstruct images with an approximate twofold increase in optical resolution. Results: using our technique, resonant 2P superresolution patterned excitation reconstruction microscopy, we demonstrate our ability to investigate nanoscopic neuronal architecture in the cerebral cortex of the mouse brain at a depth of 120 μm in vivo and 210 μm ex vivo with a resolution of 119 nm. This technique optimizes the combination of speed and depth for improved in vivo imaging in the rodent neocortex. Conclusion: this study demonstrates a potentially useful technique for superresolution in vivo investigations in the rodent brain in deep tissue, creating a platform for investigating nanoscopic neuronal dynamics. Significance : this technique optimizes the combination of speed and depth for improved superresolution in vivo imaging in the rodent neocortex. [ABSTRACT FROM PUBLISHER]

Details

Language :
English
ISSN :
00189294
Volume :
65
Issue :
1
Database :
Academic Search Index
Journal :
IEEE Transactions on Biomedical Engineering
Publication Type :
Academic Journal
Accession number :
126964029
Full Text :
https://doi.org/10.1109/TBME.2017.2773540