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Universal Adaptive Optics for Microscopy Through Embedded Neural Network Control by Qi Hu; Martin Hailstone; Jingyu Wang; Matthew Wincott; Danail Stoychev; Huriye Atilgan; Dalia Gala; Tai Chaiamarit; Richard M. Parton; Jacopo Antonello; Adam M. Packer; Ilan Davis; Martin J. Booth is a Engineering article available to read on EtoBox.

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## Abstract The resolution and contrast of microscope imaging is often affected by aberrations introduced by imperfect optical systems and inhomogeneous refractive structures in specimens. Adaptive optics (AO) compensates these aberrations and restores diffraction limited performance. A wide range of AO solutions have been introduced, often tailored to a specific microscope type or application. Until now, a universal AO solution – one that can be readily transferred between microscope modalities – has not been deployed. We propose versatile and fast aberration correction using a physics-based machine learning assisted wavefront-sensorless AO control (MLAO) method. Unlike previous ML methods, we used a specially constructed neural network (NN) architecture, designed using physical understanding of the general microscope image formation, that was embedded in the control loop of different microscope systems. The approach means that not only is the resulting NN orders of magnitude simpler than previous NN methods, but the concept is translatable across microscope modalities. We demonstrated the method on a two-photon, a three-photon and a widefield three-dimensional (3D) structured ill

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Author

Qi Hu; Martin Hailstone; Jingyu Wang; Matthew Wincott; Danail Stoychev; Huriye Atilgan; Dalia Gala; Tai Chaiamarit; Richard M. Parton; Jacopo Antonello; Adam M. Packer; Ilan Davis; Martin J. Booth

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Engineering (Physical Sciences)