Broadband CARS Hyperspectral Classification of Single Immune Cells.

Broadband CARS is a coherent Raman scattering technique that provides access to the full biological vibrational spectrum within milliseconds, facilitating the recording of widefield hyperspectral Raman images. In this work, BCARS hyperspectral images of unstained cells from two different cell lines of immune lineage (T cell [Jurkat] and pDCs [CAL-1]) were recorded and analyzed using multivariate statistical algorithms in order to determine the spectral differences between the cells. A classifier was trained which could distinguish the known cells with a 97% out-of-bag accuracy.

Removing non-resonant background from broadband CARS using a physics-informed neural network.

Broadband coherent anti-Stokes Raman scattering (BCARS) is capable of producing high-quality Raman spectra spanning broad bandwidths, 400-4000 cm, with millisecond acquisition times. Raw BCARS spectra, however, are a coherent combination of vibrationally resonant (Raman) and non-resonant (electronic) components that may challenge or degrade chemical analyses. Recently, we demonstrated a deep convolutional autoencoder network, trained on pairs of simulated BCARS-Raman datasets, which could retrieve the Raman signal with high quality under ideal conditions.