| Autor: |
Yihong Ji, Danni Chen, Hanzhe Wu, Gan Xiang, Heng Li, Bin Yu, Junle Qu |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
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| Zdroj: |
IEEE Photonics Journal, Vol 16, Iss 6, Pp 1-6 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2024.3476514 |
| Popis: |
The point-by point 3D scanning strategy adopted in Stimulated Emission Depletion Microscopy (STED) is time-consuming. The 3D scanning can be replaced with a 2D scanning in the non-diffracting Bessel-Bessel STED (BB-STED). In order to extract the excited emitters’ axial information in BB-STED, we propose to encode axial information by using a detection optical path with single-helix PSF, and then predict the depths of the emitters with deep learning. Simulation demonstrated that, for dense emitters in a depth range of 4 µm, an axial precision of ∼35 nm can be achieved. Our method also works for experimental data, and an axial precision of ∼63 nm can be achieved. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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