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Adaptive optics for structured illumination microscopy based on deep learning
Author(s) -
Zheng Yao,
Chen Jiajia,
Wu Chenxue,
Gong Wei,
Si Ke
Publication year - 2021
Publication title -
cytometry part a
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.316
H-Index - 90
eISSN - 1552-4930
pISSN - 1552-4922
DOI - 10.1002/cyto.a.24319
Subject(s) - adaptive optics , optics , zernike polynomials , computer science , artificial intelligence , microscopy , convolution (computer science) , distortion (music) , convolutional neural network , deep learning , image resolution , phase (matter) , optical aberration , resolution (logic) , image quality , computer vision , artificial neural network , physics , image (mathematics) , wavefront , computer network , bandwidth (computing) , amplifier , quantum mechanics
Abstract Structured illumination microscopy (SIM) is widely used in biological imaging for its high resolution, fast imaging speed, and simple optical setup. However, when imaging thick samples, the structured illumination patterns in SIM will suffer from optical aberrations, leading to a serious deterioration in resolution. Therefore, it is necessary to reconstruct structured illumination patterns with high quality and efficiency in deep tissue imaging. Here we demonstrate an adaptive optics (AO) correction method based on deep learning in wide‐field SIM imaging system. The mapping between the coefficients of the first 15 Zernike modes and their corresponding distorted patterns is established to train the convolution neural network (CNN). The results show that the optimized CNN can predict the aberration phase within ~10.1 ms with a personal computer. The correlation index between the aberration phases and their corresponding predicted aberration phase is up to 0.9986. This method is highly robust and effective for patterns with various spatial densities and illumination conditions and able to effectively correct the imaging distortion caused by optical aberration in SIM system.