Point spread function (PSF) engineering is an important technique to
encode the properties (e.g., 3D positions, color, and orientation) of
a single molecule in the shape of the PSF, often with the help of a
programmable phase modulator. A deformable mirror (DM) is currently
the most widely used phase modulator for fluorescence detection as it
shows negligible photon loss. However, it relies on careful
calibration for precise wavefront control. Therefore, design of an
optimal PSF not only relies on the theoretical calculation of the
maximum information content, but also the physical behavior of the
phase modulator, which is often ignored during the optimization
process. Here, we develop a framework for PSF engineering which could
generate a device specific optimal PSF for 3D super-resolution imaging
using a DM. We use our method to generate two types of PSFs with
depths of field comparable to the widely used astigmatism and tetrapod
PSFs, respectively. We demonstrate the superior performance of the DM
specific optimal PSF over the conventional astigmatism and tetrapod
PSF both theoretically and experimentally.
Stimulated emission depletion (STED) fluorescence nanoscopy allows the three-dimensional (3D) visualization of nanoscale subcellular structures, providing unique insights into their spatial organization. However, 3D-STED imaging and quantification of dense features are obstructed by the low signal-to-background ratio (SBR) resulting from optical aberrations and out-of-focus background. Here, combining adaptive optics elements, we present an easy-to-implement, flexible, and effective method to improve the SBR by dynamic phase switching. By switching to a counterclockwise vortex phase mask and a top-hat one with an incorrect inner radius, the depletion pattern features a nonzero-intensity center, enabling accurate background recordings. When the recorded background is subtracted from the aberration-corrected 3D-STED image, the SBR in dense sample areas can be improved by a factor of 3−7 times. We demonstrate our method on various dense subcellular structures, showing more advantages than the software-based background subtraction algorithms.
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