Nonlinear super-resolution imaging in living zebrafish

Optical microscopy has been indispensable for visualizing biological structure and function, while it remains a challenge since the limited diffraction resolution and restricted imaging depth. Nonlinear multifocal structured illumination microscopy (MSIM) provides resolution-doubled images and good penetration. Furthermore, adaptive optics (AO) is an effective method to recover spatial resolution and signal-to-noise ratio (SNR) in deep tissues and complex environments. Thus, we present a non-inertial scanning nonlinear MSIM system combined with AO to realize super-resolution imaging with aberration correction in vivo. Our strategy is designed to correct both laser and fluorescence paths simultaneously using a spatial light modulator and a deformable mirror respectively, providing better results than the individual path corrections. Furthermore, traditional approaches for MSIM image reconstruction at the expense of speed. Many raw images and iteration times are required for the reconstruction; besides, four steps in MSIM are separately accomplished in the reconstruction procedures of these methods. This is complicated and time-consuming, limiting extensive adoption of MSIM for practical use. To address the issues, a deep convolutional neural network to learn a direct mapping from raw MSIM images to super-resolution image, which takes advantage of the computational advances of deep learning to accelerate the reconstruction. The successful implementation of AO MSIM and fast MSIM reconstruction have allowed for the dynamic morphological characteristics of zebrafish motoneurons in vivo.