Aberration correction for real-time measurements in adaptive confocal microscopy

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Automated microscopic techniques with high spatial and temporal resolutions are required for the three-dimensional examination of biological cells or tissues. Two-photon microscopy can provide the required high resolution, but as a point-based technique it requires scans in three dimensions, resulting in limited time resolution. Adaptive optical elements in principle have the potential to break this limitation for volumetric scans. However, the use of adaptive components is accompanied by the introduction and tuning of aberrations, which can be a problem for two-photon microscopy excitation. As presented in the first two funding periods, these and sample-induced aberrations can be compensated for with adaptive lenses. Furthermore, by using adaptive lenses for an axial scan and the adaptive prism for a lateral scan, the implementation of fast 3D scans without mechanical motion is possible. In the third phase of the project, a fully adaptive, smart two-photon microscope will be implemented, which will use an adaptive microscope lens and the adaptive prism to enable fast scans, as well as dynamic correction of aberrations. For this purpose, the adaptive components from the previous project phases will first be characterized with an ultrashort pulse laser and, if necessary, adapted to the higher energy density. Then, an adaptive bi-actuator lens for aberration correction and axial scanning will be integrated into a microscope objective. This, in combination with the adaptive prism, enables correction of sample- and system-induced aberrations (defocus, astigmatism, coma and spherical aberration) as well as 3D scanning of the sample without mechanical displacement. By using piezoceramics as actuator material, hysteresis is introduced into the behaviour of the adaptive components. In addition, multiplying the actuators in an aberration-correcting 3D scan system makes the actuation much more complex than for a single adaptive lens. For such highly complex multiple-input systems, the design of a controller becomes increasingly complex and optimal control requires more and more iterations. As an alternative to classical control engineering, methods from machine learning, such as neural networks and reinforcement learning will be used to control the overall system in order to realize an automated microscope. The targeted two-photon microscope offers the possibility of miniaturization, automation and the development of compact and robust optical systems due to the new adaptive objective and the improved control. For first demonstration measurements will be applied to study the effects of goitrogens in zebrafish embryos. The result will be an important contribution to the further development of adaptive optical elements and automated smart microscopy.