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Author: Meyer, Tobias × Author: Popp, Jürgen × Author: Schmitt, Michael × Type: article × Version: publishedVersion ×

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    FLIM data analysis based on Laguerre polynomial decomposition and machine-learning
    (Bellingham, Wash. : SPIE, 2021) Guo, Shuxia; Silge, Anja; Bae, Hyeonsoo; Tolstik, Tatiana; Meyer, Tobias; Matziolis, Georg; Schmitt, Michael; Popp, Jürgen; Bocklitz, Thomas
    Significance: The potential of fluorescence lifetime imaging microscopy (FLIM) is recently being recognized, especially in biological studies. However, FLIM does not directly measure the lifetimes, rather it records the fluorescence decay traces. The lifetimes and/or abundances have to be estimated from these traces during the phase of data processing. To precisely estimate these parameters is challenging and requires a well-designed computer program. Conventionally employed methods, which are based on curve fitting, are computationally expensive and limited in performance especially for highly noisy FLIM data. The graphical analysis, while free of fit, requires calibration samples for a quantitative analysis. Aim: We propose to extract the lifetimes and abundances directly from the decay traces through machine learning (ML). Approach: The ML-based approach was verified with simulated testing data in which the lifetimes and abundances were known exactly. Thereafter, we compared its performance with the commercial software SPCImage based on datasets measured from biological samples on a time-correlated single photon counting system. We reconstructed the decay traces using the lifetime and abundance values estimated by ML and SPCImage methods and utilized the root-mean-squared-error (RMSE) as marker. Results: The RMSE, which represents the difference between the reconstructed and measured decay traces, was observed to be lower for ML than for SPCImage. In addition, we could demonstrate with a three-component analysis the high potential and flexibility of the ML method to deal with more than two lifetime components.
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    Multimodal Nonlinear Microscopy for Therapy Monitoring of Cold Atmospheric Plasma Treatment
    (Basel : MDPI, 2019) Meyer, Tobias; Bae, Hyeonsoo; Hasse, Sybille; Winter, Jörn; von Woedtke, Thomas; Schmitt, Michael; Weltmann, Klaus-Dieter; Popp, Jürgen
    Here we report on a non-linear spectroscopic method for visualization of cold atmospheric plasma (CAP)-induced changes in tissue for reaching a new quality level of CAP application in medicine via online monitoring of wound or cancer treatment. A combination of coherent anti-Stokes Raman scattering (CARS), two-photon fluorescence lifetime imaging (2P-FLIM) and second harmonic generation (SHG) microscopy has been used for non-invasive and label-free detection of CAP-induced changes on human skin and mucosa samples. By correlation with histochemical staining, the observed local increase in fluorescence could be assigned to melanin. CARS and SHG prove the integrity of the tissue structure, visualize tissue morphology and composition. The influence of plasma effects by variation of plasma parameters e.g., duration of treatment, gas composition and plasma source has been evaluated. Overall quantitative spectroscopic markers could be identified for a direct monitoring of CAP-treated tissue areas, which is very important for translating CAPs into clinical routine.