Localizing axial dense emitters based on single-helix point spread function and deep learning
Abstract
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.
Keywords
Cite
@article{arxiv.2402.06863,
title = {Localizing axial dense emitters based on single-helix point spread function and deep learning},
author = {Yihong Ji and Danni Chen and Hanzhe Wu and Gan Xiang and Heng Li and Bin Yu and Junle Qu},
journal= {arXiv preprint arXiv:2402.06863},
year = {2024}
}