Deep learning spectroscopic stimulated Raman scattering microscopy

Haonan Lin; Fengyuan Deng; Chi Zhang; Cheng Zong; Ji-Xin Cheng

doi:10.1117/12.2508999

22 February 2019 Deep learning spectroscopic stimulated Raman scattering microscopy

Haonan Lin, Fengyuan Deng, Chi Zhang, Cheng Zong, Ji-Xin Cheng

Proceedings Volume 10882, Multiphoton Microscopy in the Biomedical Sciences XIX; 108822B (2019) https://doi.org/10.1117/12.2508999
Event: SPIE BiOS, 2019, San Francisco, California, United States

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.

Citation Download Citation

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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