Abstract
Raman scattering based imaging represents a very powerful optical tool for biomedical diagnostics. Different Raman signatures obtained by distinct tissue structures and disease induced changes provoke sophisticated analysis of the hyperspectral Raman datasets. While the analysis of linear Raman spectroscopic tissue data is quite established, the evaluation of hyperspectral nonlinear Raman data has not yet been evaluated in great detail. The two most common nonlinear Raman methods are CARS (coherent anti-Stokes Raman scattering) and SRS (stimulated Raman scattering) spectroscopy. Specifically the linear concentration dependence of SRS as compared to the quadratic dependence of CARS has fostered the application of SRS tissue imaging. Here, we applied spectral processing to hyperspectral SRS and CARS data for tissue characterization. We could demonstrate for the first time that similar cluster distributions can be obtained for multispectral CARS and SRS data but that clustering is based on different spectral features due to interference effects in CARS and the different concentration dependence of CARS and SRS. It is shown that a direct combination of CARS and SRS data does not improve the clustering results.
Highlights
Label-free imaging based on vibrational spectroscopy represents a powerful method to investigate the spatial distribution of various molecules in complex samples, biomedical specimens.[1]
IR imaging is limited by the water absorption and low spatial resolution, even though recent concepts have improved on the spatial resolution.[2]
This is due to the fact that changes in the spectral shape or shifts in the peak position cannot be extracted from few images but require spectral information
Summary
Label-free imaging based on vibrational spectroscopy represents a powerful method to investigate the spatial distribution of various molecules in complex samples, biomedical specimens.[1]. Appropriate spectral pre-processing steps to correct for background signals and other illumination artefacts are needed.[10,12] In this contribution, we have simultaneously recorded hyperspectral CARS and SRS datasets for head and neck tissue samples, analyzed the datasets by multispectral data analysis approaches, and compared the results.
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