Localizing axial dense emitters based on single-helix point spread function and deep learning
arxiv(2024)
摘要
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.
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