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Author: Bocklitz, Thomas W. × Author: Popp, Jürgen × Author: Schmitt, Michael × End date: 2019 × Start date: 2014 ×

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    Pseudo-HE images derived from CARS/TPEF/SHG multimodal imaging in combination with Raman-spectroscopy as a pathological screening tool
    (London : BioMed Central, 2016) Bocklitz, Thomas W.; Salah, Firas Subhi; Vogler, Nadine; Heuke, Sandro; Chernavskaia, Olga; Schmidt, Carsten; Waldner, Maximilian J.; Greten, Florian R.; Bräuer, Rolf; Schmitt, Michael; Stallmach, Andreas; Petersen, Iver; Popp, Jürgen
    Due to the steadily increasing number of cancer patients worldwide the early diagnosis and treatment of cancer is a major field of research. The diagnosis of cancer is mostly performed by an experienced pathologist via the visual inspection of histo-pathological stained tissue sections. To save valuable time, low quality cryosections are frequently analyzed with diagnostic accuracies that are below those of high quality embedded tissue sections. Thus, alternative means have to be found that enable for fast and accurate diagnosis as the basis of following clinical decision making.
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    Comparison of hyperspectral coherent Raman scattering microscopies for biomedical applications
    (College Park : American Institute of Physics, 2018) Bocklitz, Thomas W.; Meyer, Tobias; Schmitt, Michael; Rimke, Ingo; Hoffmann, Franziska; von Eggeling, Ferdinand; Ernst, G.; Guntinas-Lichius, Orlando; Popp, Jürgen
    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.