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End date: 2023 × Start date: 2010 × DDC: 610 × Has files: Yes × Subject: Coherent scattering × Subject: Raman spectroscopy × WGL Institute: IPHT ×

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    Multimodal nonlinear imaging of atherosclerotic plaques differentiation of triglyceride and cholesterol deposits
    (Singapore [u.a.] : World Scientific Publishing, 2014) Matthäus, C.; Cicchi, R.; Meyer, T.; Lattermann, A.; Schmitt, M.; Romeike, B.F.M.; Krafft, C.; Dietzek, B.; Brehm, B.R.; Pavone, F.S.; Popp, J.
    Cardiovascular diseases in general and atherothrombosis as the most common of its individual disease entities is the leading cause of death in the developed countries. Therefore, visualization and characterization of inner arterial plaque composition is of vital diagnostic interest, especially for the early recognition of vulnerable plaques. Established clinical techniques provide valuable morphological information but cannot deliver information about the chemical composition of individual plaques. Therefore, spectroscopic imaging techniques have recently drawn considerable attention. Based on the spectroscopic properties of the individual plaque components, as for instance different types of lipids, the composition of atherosclerotic plaques can be analyzed qualitatively as well as quantitatively. Here, we compare the feasibility of multimodal nonlinear imaging combining two-photon fluorescence (TPF), coherent anti-Stokes Raman scattering (CARS) and second-harmonic generation (SHG) microscopy to contrast composition and morphology of lipid deposits against the surrounding matrix of connective tissue with diffraction limited spatial resolution. In this contribution, the spatial distribution of major constituents of the arterial wall and atherosclerotic plaques like elastin, collagen, triglycerides and cholesterol can be simultaneously visualized by a combination of nonlinear imaging methods, providing a powerful label-free complement to standard histopathological methods with great potential for in vivo application.
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    Computational tissue staining of non-linear multimodal imaging using supervised and unsupervised deep learning
    (Washington, DC : OSA, 2021) Pradhan, Pranita; Meyer, Tobias; Vieth, Michael; Stallmach, Andreas; Waldner, Maximilian; Schmitt, Michael; Popp, Juergen; Bocklitz, Thomas
    Hematoxylin and Eosin (H&E) staining is the 'gold-standard' method in histopathology. However, standard H&E staining of high-quality tissue sections requires long sample preparation times including sample embedding, which restricts its application for 'real-time' disease diagnosis. Due to this reason, a label-free alternative technique like non-linear multimodal (NLM) imaging, which is the combination of three non-linear optical modalities including coherent anti-Stokes Raman scattering, two-photon excitation fluorescence and second-harmonic generation, is proposed in this work. To correlate the information of the NLM images with H&E images, this work proposes computational staining of NLM images using deep learning models in a supervised and an unsupervised approach. In the supervised and the unsupervised approach, conditional generative adversarial networks (CGANs) and cycle conditional generative adversarial networks (cycle CGANs) are used, respectively. Both CGAN and cycle CGAN models generate pseudo H&E images, which are quantitatively analyzed based on mean squared error, structure similarity index and color shading similarity index. The mean of the three metrics calculated for the computationally generated H&E images indicate significant performance. Thus, utilizing CGAN and cycle CGAN models for computational staining is beneficial for diagnostic applications without performing a laboratory-based staining procedure. To the author's best knowledge, it is the first time that NLM images are computationally stained to H&E images using GANs in an unsupervised manner.