Localizing axial dense emitters based on single-helix point spread function and deep learning

TL;DR

This paper introduces a deep learning approach combined with a single-helix PSF to accurately localize dense axial emitters in STED microscopy, significantly improving axial resolution in 3D imaging.

Contribution

It proposes a novel method that encodes axial information using SH-PSF and deep learning, enabling precise 3D localization of dense emitters in STED microscopy.

Findings

Achieves ~35 nm axial precision in simulations for 4 nm depth range.

Attains ~63 nm axial precision on experimental data.

Effective for dense emitter localization in 3D STED imaging.

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.