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Wavefront correction for adaptive optics with reflected light and deep neural networks.

Ivan Vishniakou, Johannes D Seelig

|May 15, 2020

Summary

This study introduces a novel adaptive optics method using deep neural networks to correct aberrations in reflected light microscopy. This technique enhances imaging quality in scattering biological tissues.

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Area of Science:

Biomedical Optics
Microscopy
Computational Imaging

Background:

Light scattering and aberrations impede optical microscopy in biological tissues.
Adaptive optics (AO) techniques are crucial for overcoming these limitations.
Existing AO methods often require specific configurations or are complex to implement.

Purpose of the Study:

To develop a wavefront correction method for AO microscopy using reflected light.
To integrate deep neural networks (DNNs) for aberration correction in an epi-detection configuration.
To enable independent correction of excitation and detection aberrations.

Main Methods:

Generation of large datasets of sample aberrations and corresponding reflected focus images.
Training deep neural networks on these datasets to learn aberration patterns.
Application of trained DNNs for disentangling and correcting excitation and detection aberrations.
Validation using two-photon imaging and scattering guide stars.

Main Results:

The developed DNNs successfully corrected aberrations based on reflected light images from scattering samples.
Independent correction of excitation and detection path aberrations was achieved.
The method demonstrated effectiveness with both sample-induced aberrations and scattering guide stars.
Validated aberration corrections showed improved imaging performance in two-photon microscopy.

Conclusions:

Deep learning-based adaptive optics using reflected light offers a powerful solution for aberration correction in scattering biological tissues.
The epi-detection compatible method simplifies AO implementation.
This approach significantly enhances the resolution and clarity of optical microscopy in challenging biological samples.