  Today the gold standard for in vivo imaging through scattering tissue is the
point-scanning two-photon microscope (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 spatial resolution and signal-to-noise
ratio (SNR), burying fluorescent signals from small structures such as
dendritic spines. In this work, we present a data-driven deep learning approach
to improve resolution and SNR 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-resolution through scattering media. We
demonstrate this approach for in vivo imaging of dendritic spines on pyramidal
neurons in the mouse visual cortex. We show that our trained network rapidly
outputs high-resolution 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 3D convolution neural network is one
to two orders of magnitude faster than PSTPM but retains the high resolution
and SNR necessary to analyze small fluorescent structures. The proposed 3D
convolution deep network could also be potentially beneficial for improving the
performance of many speed-demanding deep-tissue imaging applications such as in
vivo voltage imaging.
