Wavefront estimation through structured detection in laser scanning microscopy

Abstract

Laser scanning microscopy (LSM) is the base of numerous advanced imaging techniques, including confocal laser scanning microscopy (CLSM), a widely used tool in life sciences research. However, its effective resolution is often compromised by optical aberrations, a common challenge in all optical systems. While adaptive optics (AO) can correct these aberrations, current methods face significant limitations: Aberration estimation, which is central to any AO approach, typically requires specialized hardware or prolonged sample exposure, rendering these methods sample-invasive, and less user-friendly. In this study, we introduce a simple and efficient AO approach for CLSM systems equipped with a detector array - the same of super-resolved image-scanning microscopy - and an AO element for beam shaping. We demonstrate that imaging datasets acquired with a detector array inherently encode aberration information. Leveraging this property, we developed a custom convolutional neural network capable of decoding aberrations, up to the 11th Zernike coefficient, directly from a single acquisition. This method enables a new generation of AO implementations for LSM, offering an accessible solution that minimizes sample stress while achieving high-resolution, and aberration-free imaging.