Deep learning (DL) has been recently applied to adaptive optics (AO) to correct optical aberrations rapidly in biomedical imaging. Here we propose a DL assisted zonal adaptive correction method to perform corrections of high degrees of freedom while maintaining the fast speed. With a trained DL neural network, the pattern on the correction device which is divided into multiple zone phase elements can be directly inferred from the aberration distorted point-spread function image in this method. The inference can be completed in 12.6 ms with the average mean square error 0.88 when 224 zones are used. The results show a good performance on aberrations of different complexities. Since no extra device is required, this method has potentials in deep tissue imaging and large volume imaging.

The combination of light sheet fluorescence microscopy (LSFM) and the optical clearing method can achieve fast three-dimensional high-resolution imaging. However, there is an essential contradiction between the field of view (FoV) and spatial resolution. Also, aberration and scattering still exist after tissue clearing, which seriously limits the imaging depth of LSFM. Here we propose a Schwartz modulation method and implement it in LSFM based on a quasi-Bessel beam to enlarge the imaging FoV without sacrificing its spatial resolution. The simulation results show that the FoV of the LSFM is enlarged by a factor of 1.73 compared to the Bessel beam. The capability of extremely fast decay along the optical axis makes Schwartz modulation more tolerant for scattering, indicating potential applications for deep tissue imaging. Also, the capability of sidelobe suppression effectively decreases unnecessary fluorescence excitation and photobleaching.

Adaptive optics has been widely used in biological science to recover high-resolution optical image deep into the tissue, where optical distortion detection with high speed and accuracy is strongly required. Here, we introduce convolutional neural networks, one of the most popular machine learning models, into Shack–Hartmann wavefront sensor (SHWS) to simplify optical distortion detection processes. Without image segmentation or centroid positioning algorithm, the trained network could estimate up to 36th Zernike mode coefficients directly from a full SHWS image within 1.227[Formula: see text]ms on a personal computer, and achieves prediction accuracy up to 97.4%. The simulation results show that the average root mean squared error in phase residuals of our method is 75.64% lower than that with the modal-based SHWS method. With the high detection accuracy and simplified detection processes, this work has the potential to be applied in wavefront sensor-based adaptive optics for in vivo deep tissue imaging.

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Two-photon microscopy normally suffers from the scattering of the tissue in biological imaging. Multidither coherent optical adaptive technique (COAT) can correct the scattered wavefront in parallel. However, the determination of the corrective phases may not be completely accurate using conventional method, which undermines the performance of this technique. In this paper, we theoretically demonstrate a method that can obtain more accurate corrective phases by determining the phase values from the square root of the fluorescence signal. A numerical simulation model is established to study the performance of adaptive optics in two-photon microscopy by combining scalar diffraction theory with vector diffraction theory. The results show that the distortion of the wavefront can be corrected more thoroughly with our method in two-photon imaging. In our simulation, with the scattering from a 450-[Formula: see text]m-thick mouse brain tissue, excitation focal spots with higher peak-to-background ratio (PBR) and images with higher contrast can be obtained. Hence, further enhancement of the multidither COAT correction performance in two-photon imaging can be expected.

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