We present a novel approach to balanced-detection stimulated Raman scattering (SRS) spectroscopy and microscopy, called In-line Balanced Detection (IBD). IBD-SRS not only completely removes high-frequency laser fluctuations, a crucial ingredient to improve signal-to-noise ratio in modulation-transfer techniques, but also passively and automatically balances the low-frequency signal variation due to spatially varying sample transmission. It takes advantage of polarization multiplexing and an inherently stable common-path geometry. A birefringent crystal before the sample creates two orthogonally polarized and collinear replicas of the Stokes pulse, with ~10-ps relative delay. The first serves as a reference pulse; the second, temporally overlapped with the pump pulse, probes the Raman response of the sample. As reference and probe pulses cross the sample at the same position, they maintain their balance during image acquisition. IBD can be implemented in any conventional SRS setup, by simply adding a few passive optical elements in the beam. We have tested its performances on a home-built multimodal laser-scanning microscope, coupled with a compact fiber-format laser source. We obtain common-mode noise rejection up to 30 dB with respect to the unbalanced case, thus reaching shot-noise-limited detection, without the need of any electronic (active) auto-balancing. We have employed IBD-SRS to distinguish different polymer beads, to locate lignin and cellulose in the walls of plant cells and to visualize the three-dimensional distribution of lipids in HuH7 and HepaRG hepatic cells. To demonstrate the suitability of IBD-SRS in scattering environments, we show a significant image-quality improvement also when measuring lipids in thick bovine liver tissues.
Dario Polli, Francesco Crisafi, Vikas Kumar, and Giulio Cerullo, "In-line balanced detection stimulated Raman scattering microscopy from a compact fiber-format laser source (Conference Presentation)," Proc. SPIE 10505, High-Speed Biomedical Imaging and Spectroscopy III: Toward Big Data Instrumentation and Management, 105050O (Presented at SPIE BiOS: January 30, 2018; Published: 15 March 2018); https://doi.org/10.1117/12.2288393.5752179456001.
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