Adaptive optics with reflected light and deep neural networks
This work addresses limitations in biological tissue imaging for microscopy applications, representing an incremental improvement in adaptive optics techniques.
The researchers tackled the problem of light scattering and aberrations in optical microscopy by developing an adaptive optics method using reflected light and deep neural networks, which successfully disentangled and corrected excitation and detection aberrations, validated through two-photon imaging.
Light scattering and aberrations limit optical microscopy in biological tissue, which motivates the development of adaptive optics techniques. Here, we develop a method for adaptive optics with reflected light and deep neural networks compatible with an epi-detection configuration. Large datasets of sample aberrations which consist of excitation and detection path aberrations as well as the corresponding reflected focus images are generated. These datasets are used for training deep neural networks. After training, these networks can disentangle and independently correct excitation and detection aberrations based on reflected light images recorded from scattering samples. A similar deep learning approach is also demonstrated with scattering guide stars. The predicted aberration corrections are validated using two photon imaging.