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22 February 2019 Deep learning spectroscopic stimulated Raman scattering microscopy
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Abstract
Spectroscopic stimulated Raman scattering (SRS) is a label-free chemical imaging modality enabling visualization of molecules in living systems with high specificity. Among various spectroscopic SRS imaging methods, a convenient way is through linearly chirping two femtosecond lasers and tuning their temporal delay, which in turn corresponds to different Raman shifts. Currently, the acquisition speed using a resonant mirror is 3 seconds (80 microseconds per spectrum), which is insufficient for imaging samples with high motility. In this work, we aim to push the imaging speed using a 50-kHz polygon scanner as a delay line tuner, achieving a speed of 20 microseconds per spectrum. At such high speeds, to overcome the signal level decrease due to reduced signal integration time, we apply a U-Net deep learning framework, which first takes pairs of spectroscopic SRS images at different speeds as training samples, with high-speed, low-signal images as input and low speed, high-signal ones as output. After training, the network is capable of rapidly transforming a low-signal spectroscopic image to a high-signal version. Consequently, our design can generate ultrafast spectroscopic SRS image while maintaining the signal level comparable to the output with longer signal integration time.
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Haonan Lin, Fengyuan Deng, Chi Zhang, Cheng Zong, and Ji-Xin Cheng "Deep learning spectroscopic stimulated Raman scattering microscopy", Proc. SPIE 10882, Multiphoton Microscopy in the Biomedical Sciences XIX, 108822B (22 February 2019); https://doi.org/10.1117/12.2508999
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