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Abstract

Control of light through a microscope objective with a high numerical
aperture is a common requirement in applications such as optogenetics,
adaptive optics, or laser processing. Light propagation, including
polarization effects, can be described under these conditions using the
Debye-Wolf diffraction integral. Here, we take advantage of
differentiable optimization , machine learning for efficiently
optimizing the Debye-Wolf integral for such applications. For light
shaping we show that this optimization approach is suitable for
engineering arbitrary three-dimensional point spread functions in a
two-photon microscope. For differentiable model -based adaptive optics
(DAO), the developed method can find aberration corrections with
intrinsic image features, for example neurons labeled with genetically
encoded calcium indicators, without requiring guide stars. Using
computational modeling we further discuss the range of spatial
frequencies and magnitudes of aberrations which can be corrected with
this approach.(c) 2023 Optica Publishing Group under the terms of the
Optica Open Access Publishing Agreement