Deep learning enables fast, gentle STED microscopy

2023 | journal article. A publication with affiliation to the University of Göttingen.

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​Deep learning enables fast, gentle STED microscopy​
Ebrahimi, V.; Stephan, T.; Kim, J.; Carravilla, P.; Eggeling, C.; Jakobs, S. & Han, K. Y.​ (2023) 
Communications Biology6(1).​ DOI: https://doi.org/10.1038/s42003-023-05054-z 

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Authors
Ebrahimi, Vahid; Stephan, Till; Kim, Jiah; Carravilla, Pablo; Eggeling, Christian; Jakobs, Stefan; Han, Kyu Young
Abstract
Abstract STED microscopy is widely used to image subcellular structures with super-resolution. Here, we report that restoring STED images with deep learning can mitigate photobleaching and photodamage by reducing the pixel dwell time by one or two orders of magnitude. Our method allows for efficient and robust restoration of noisy 2D and 3D STED images with multiple targets and facilitates long-term imaging of mitochondrial dynamics.
Issue Date
2023
Journal
Communications Biology 
Project
SFB 1286: Quantitative Synaptologie 
SFB 1286 | A05: Mitochondriale Heterogenität in Synapsen 
Working Group
RG Jakobs (Structure and Dynamics of Mitochondria) 
External URL
https://sfb1286.uni-goettingen.de/literature/publications/207
eISSN
2399-3642
Language
English
Sponsor
U.S. Department of Health & Human Services | National Institutes of Health https://doi.org/10.13039/100000002
U.S. Department of Health & Human Services | National Institutes of Health

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