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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.06863 |
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| _version_ | 1866911789491421184 |
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| author | Ji, Yihong Chen, Danni Wu, Hanzhe Xiang, Gan Li, Heng Yu, Bin Qu, Junle |
| author_facet | Ji, Yihong Chen, Danni Wu, Hanzhe Xiang, Gan Li, Heng Yu, Bin Qu, Junle |
| contents | Stimulated Emission Depletion Microscopy (STED) can achieve a spatial resolution as high as several nanometers. As a point scanning imaging method, it requires 3D scanning to complete the imaging of 3D samples. The time-consuming 3D scanning can be compressed into a 2D one in the non-diffracting Bessel-Bessel STED (BB-STED) where samples are effectively excited by an optical needle. However, the image is just the 2D projection, i.e., there is no real axial resolution. Therefore, we propose a method to encode axial information to axially dense emitters by using a detection optical path with single-helix point spread function (SH-PSF), and then predicted the depths of the emitters by means of deep learning. Simulation demonstrated that, for a density 1~ 20 emitters in a depth range of 4 nm, 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06863 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Localizing axial dense emitters based on single-helix point spread function and deep learning Ji, Yihong Chen, Danni Wu, Hanzhe Xiang, Gan Li, Heng Yu, Bin Qu, Junle Optics Stimulated Emission Depletion Microscopy (STED) can achieve a spatial resolution as high as several nanometers. As a point scanning imaging method, it requires 3D scanning to complete the imaging of 3D samples. The time-consuming 3D scanning can be compressed into a 2D one in the non-diffracting Bessel-Bessel STED (BB-STED) where samples are effectively excited by an optical needle. However, the image is just the 2D projection, i.e., there is no real axial resolution. Therefore, we propose a method to encode axial information to axially dense emitters by using a detection optical path with single-helix point spread function (SH-PSF), and then predicted the depths of the emitters by means of deep learning. Simulation demonstrated that, for a density 1~ 20 emitters in a depth range of 4 nm, 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. |
| title | Localizing axial dense emitters based on single-helix point spread function and deep learning |
| topic | Optics |
| url | https://arxiv.org/abs/2402.06863 |