Localizing Axial Dense Emitters Based on Single-Helix Point Spread Function and Deep Learning

Abstract

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.