High-speed broadband CARS in the fingerprint region through supercontinuum generation in bulk media

Abstract

Spontaneous Raman spectroscopy is a powerful label-free and non-invasive imaging technique for mapping cells and tissues, delivering relevant biochemical information. However, in its standard implementation, the spontaneous Raman cross sections are too low, thus preventing high-speed microscopy, especially in the more informative low wavenumber region, also known as fingerprint region. Coherent Raman microscopy overcomes this hurdle providing several orders of magnitude higher speed thanks to the coherent excitation of the vibrational modes in the laser focus. In this work, we present a novel approach to broadband coherent anti-Stokes Raman scattering (B-CARS) that allows acquiring the entire fingerprint vibrational response of the sample at an unprecedented speed. The system is based on an amplified Ytterbium laser at 2 MHz repetition rate, that provides sufficient pulse energies to generate broadband near-infrared white-light supercontinuum in bulk media that we employ as broadband Stokes pulses. Coupled to narrowband pump pulses at 1035 nm, we demonstrate B-CARS microscopy down to 1 ms pixel dwell time with a diffraction-limited spatial resolution over large field of views. To extract the maximum amount of information, we enhance the signal to noise ratio of the vibrational spectra via artificial intelligence-based methods. In particular, we developed a convolutional neural network trained on data-augmented experimental input-output pairs of B-CARS spectra. Traditional algorithms are then used to remove the non-resonant background, extrapolating the pure vibrational response, and to perform chemometric analysis on the hypercubes. We test the setup performances by imaging heterogeneous biological systems, such as tissue slices of murine spine.