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 propose a simple and efficient AO strategy for CLSM systems equipped with a detector array – image-scanning microscopy – and an AO element for beam shaping. We demonstrate, for the first time, that datasets acquired with a detector array inherently encode aberration information. As a proof-of-concept of this important property, we designed a custom convolutional neural network capable of decoding aberrations up to the 11th Zernike coefficient, directly from a single acquisition. While this data-driven approach represents an initial exploration of the aberration content, it opens the door to more advanced decoding strategies – including model-based methods. This work establishes a new paradigm for aberration sensing in LSM and is designed to work synergistically with conventional AO approaches such as phase diversity, enabling faster, less invasive, and more accessible high-resolution imaging.
