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We present innovative approaches to broadband coherent Raman scattering microscopy, both in the stimulated Raman scattering (SRS) and broadband coherent anti-Stokes Raman scattering (CARS) modalities. A convolutional neural network removes the unwanted non-resonant background from broadband CARS spectra. The deep-learning model processes experimental data in 100microseconds and correctly retrieves all the relevant vibrational peaks without any user intervention or independent background measurement. A multi-channel lock-in amplifier, in combination with in-line balanced detection and a broadband optical parametric oscillator, allows sensitive measurement of the SRS spectrum at 32 frequencies in parallel, with pixel dwell times as short as 40 microseconds.
Alejandro De la Cadena,Carlo Valensise,Federico Vernuccio,Dario Polli, andGiulio N. Cerullo
"Novel approaches to broadband coherent Raman imaging", Proc. SPIE 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021, 116560O (5 March 2021); https://doi.org/10.1117/12.2578587
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Alejandro De la Cadena, Carlo Valensise, Federico Vernuccio, Dario Polli, Giulio N. Cerullo, "Novel approaches to broadband coherent Raman imaging," Proc. SPIE 11656, Advanced Chemical Microscopy for Life Science and Translational Medicine 2021, 116560O (5 March 2021); https://doi.org/10.1117/12.2578587