De-scattering Deep Neural Network Enables Fast Imaging of Spines through Scattering Media by Temporal Focusing Microscopy.

Today the gold standard for in vivo imaging through scattering tissue is point-scanning two-photon microscopy (PSTPM). Especially in neuroscience, PSTPM is widely used for deep-tissue imaging in the brain. However, due to sequential scanning, PSTPM is slow. Temporal focusing microscopy (TFM), on the other hand, focuses femtosecond pulsed laser light temporally while keeping wide-field illumination, and is consequently much faster. However, due to the use of a camera detector, TFM suffers from the scattering of emission photons. As a result, TFM produces images of poor quality, obscuring fluorescent signals from small structures such as dendritic spines. In this work, we present a de-scattering deep neural network (DeScatterNet) to improve the quality of TFM images. Using a 3D convolutional neural network (CNN) we build a map from TFM to PSTPM modalities, to enable fast TFM imaging while maintaining high image quality through scattering media. We demonstrate this approach for in vivo imaging of dendritic spines on pyramidal neurons in the mouse visual cortex. We quantitatively show that our trained network rapidly outputs images that recover biologically relevant features previously buried in the scattered fluorescence in the TFM images. In vivo imaging that combines TFM and the proposed neural network is one to two orders of magnitude faster than PSTPM but retains the high quality necessary to analyze small fluorescent structures. The proposed approach could also be beneficial for improving the performance of many speed-demanding deep-tissue imaging applications, such as in vivo voltage imaging.