Invited Article: Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications

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.

Imaging approaches for monitoring three‐dimensional cell and tissue culture systems

“…Limitations exist in the CARS non-resonant background signal, reducing detection sensitivity and distorting spectral assignment [110,111]. Bocklitz et al conducted a comparative study of hyperspectral SRS and CARS of human tissues, finding that cluster distributions in SRS and CARS are based on different spectral features, such that the combination of CARS and SRS data does not improve structure characterisation [134]. For further reading, Li et al published a recent comprehensive review of CARS that illustrated the range of applications in cell imaging, tissue imaging, biomedicine and material science [146].…”

Mammalian cell and tissue imaging using Raman and coherent Raman microscopy

“…In general, coherent scattering approaches such as hsSRS and hsCARS are often employed because they not only have a better SNR, they can capture time‐resolve spectral changes to reflect dynamic biological environments, as well as have increased scanning speed for larger samples (Bocklitz et al, 2018; Prince et al, 2017). Video‐rate quality of 30 fps or greater, corresponding to pixel dwell times of 100 ns or less, can be achieved while maintaining subcellular resolution, making SRS and CARS attractive to measuring biological cells and dynamics as well (Bocklitz et al, 2018). To reduce noises, spectral filters in CARS systems, or modulating signals for detection >1 MHz in SRS systems can be applied (Prince et al, 2017).…”

“…In clinical cases where fast and accurate diagnoses are critical to prognoses, an esoteric and sluggish acquisition is out of the question. In general, coherent scattering approaches such as hsSRS and hsCARS are often employed because they not only have a better SNR, they can capture time-resolve spectral changes to reflect dynamic biological environments, as well as have increased scanning speed for larger samples (Bocklitz et al, 2018;Prince et al, 2017). Video-rate quality of 30 fps or greater, corresponding to pixel dwell times of 100 ns or less, can be achieved while maintaining subcellular resolution, making SRS and CARS attractive to measuring biological cells and dynamics as well (Bocklitz et al, 2018).…”

Intraoperative assessment of skull base tumors using stimulated Raman scattering microscopy

“…Similar to SRS, CARS microscopy has also been explored for cancer diagnosis 20–22 based on chemical contrasts. Compared to SRH, CARS is further complicated by the presence of a non-resonant background 23–26 , which prevents direct conversion of CARS images to H&E images. Adding additional chemical contrasts from second harmonic generation and two-photon fluorescence, it has been shown that multicolor images can be generated and provides useful diagnostic information 27 .…”