No abstract

Spontaneous Raman microscopy reveals the chemical composition of a sample in a label-free and non-invasive fashion by directly measuring the vibrational spectra of molecules. However, its extremely low cross section prevents its application to fast imaging. Stimulated Raman scattering (SRS) amplifies the signal by several orders of magnitude thanks to the coherent nature of the nonlinear process, thus unlocking high-speed microscopy applications that provide analytical information to elucidate biochemical mechanisms with subcellular resolution. Nevertheless, in its standard implementation, narrowband SRS provides images at only one frequency at a time, which is not sufficient to distinguish constituents with overlapping Raman bands. Here, we report a broadband SRS microscope equipped with a home-built multichannel lock-in amplifier simultaneously measuring the SRS signal at 32 frequencies with integration time down to 44 µs, allowing for detailed, high spatial resolution mapping of spectrally congested samples. We demonstrate the capability of our microscope to differentiate the chemical constituents of heterogeneous samples by measuring the relative concentrations of different fatty acids in cultured hepatocytes at the single lipid droplet level and by differentiating tumor from peritumoral tissue in a preclinical mouse model of fibrosarcoma.

We introduce a broadband coherent anti-Stokes Raman scattering (CARS) microscope based on a 2-MHz repetition rate ytterbium laser generating 1035-nm high-energy (≈µJ level) femtosecond pulses. These features of the driving laser allow producing broadband red-shifted Stokes pulses, covering the whole fingerprint region (400–1800 cm−1), employing supercontinuum generation in a bulk crystal. Our system reaches state-of-the-art acquisition speed (<1 ms/pixel) and unprecedented sensitivity of ≈14.1 mmol/L when detecting dimethyl sulfoxide in water. To further improve the performance of the system and to enhance the signal-to-noise ratio of the CARS spectra, we designed a convolutional neural network for spectral denoising, coupled with a post-processing pipeline to distinguish different chemical species of biological tissues.

The last decades saw a huge rise of artificial intelligence (AI) as a powerful tool to boost industrial and scientific research in a broad range of fields. AI and photonics are developing a promising two‐way synergy: on the one hand, AI approaches can be used to control a number of complex linear and nonlinear photonic processes, both in the classical and quantum regimes; on the other hand, photonics can pave the way for a new class of platforms to accelerate AI‐tasks. This review provides the reader with the fundamental notions of machine learning (ML) and neural networks (NNs) and presents the main AI applications in the fields of spectroscopy and chemometrics, computational imaging (CI), wavefront shaping and quantum optics. The review concludes with an overview of future developments of the promising synergy between AI and photonics.

Coherent anti-Stokes Raman scattering (CARS) microscopy is an emerging nonlinear vibrational imaging technique that delivers label-free chemical maps of cells and tissues. In narrowband CARS, two spatiotemporally superimposed picosecond pulses, pump and Stokes, illuminate the sample to interrogate a single vibrational mode. Broadband CARS (BCARS) combines narrowband pump pulses with broadband Stokes pulses to record broad vibrational spectra. Despite recent technological advancements, BCARS microscopes still struggle to image biological samples over the entire Raman-active region (400–3100 cm–1). Here, we demonstrate a robust BCARS platform that answers this need. Our system is based on a femtosecond ytterbium laser at a 1035 nm wavelength and a 2 MHz repetition rate, which delivers high-energy pulses used to produce broadband Stokes pulses by white-light continuum generation in a bulk YAG crystal. Combining such pulses, pre-compressed to sub-20 fs duration, with narrowband pump pulses, we generate a CARS signal with a high (<9 cm–1) spectral resolution in the whole Raman-active window, exploiting both the two-color and three-color excitation mechanisms. Aided by an innovative post-processing pipeline, our microscope allows us to perform high-speed (≈1 ms pixel dwell time) imaging over a large field of view, identifying the main chemical compounds in cancer cells and discriminating tumorous from healthy regions in liver slices of mouse models, paving the way for applications in histopathological settings.