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Author: Förster, Ronny × Subject: Fluorescence microscopy ×

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Now showing 1 - 2 of 2
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    Successful optimization of reconstruction parameters in structured illumination microscopy
    (Amsterdam [u.a.] : Elsevier, 2019) Karras, Christian; Smedh, Maria; Förster, Ronny; Deschout, Hendrik; Fernandez-Rodriguez, Julia; Heintzmann, Rainer
    The impact of the different reconstruction parameters in super-resolution structured illumination microscopy (SIM) on image artifacts is carefully analyzed. These parameters comprise the Wiener filter parameter, an apodization function, zero-frequency suppression and modifications of the optical transfer function. A detailed investigation of the reconstructed image spectrum is concluded to be suitable for identifying artifacts. For this purpose, two samples, an artificial test slide and a more realistic biological system, were used to characterize the artifact classes and their correlation with the image spectra as well as the reconstruction parameters. In addition, a guideline for efficient parameter optimization is suggested and the implementation of the parameters in selected up-to-date processing packages (proprietary and open-source) is depicted. © 2018 The Authors
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    Motion artefact detection in structured illumination microscopy for live cell imaging
    (Washington, DC : Optical Society of America, 2016) Förster, Ronny; Wicker, Kai; Müller, Walter; Jost, Aurélie; Heintzmann, Rainer
    The reconstruction process of structured illumination microscopy (SIM) creates substantial artefacts if the specimen has moved during the acquisition. This reduces the applicability of SIM for live cell imaging, because these artefacts cannot always be recognized as such in the final image. A movement is not necessarily visible in the raw data, due to the varying excitation patterns and the photon noise. We present a method to detect motion by extracting and comparing two independent 3D wide-field images out of the standard SIM raw data without needing additional images. Their difference reveals moving objects overlaid with noise, which are distinguished by a probability theory-based analysis. Our algorithm tags motion-artefacts in the final high-resolution image for the first time, preventing the end-user from misinterpreting the data. We show and explain different types of artefacts and demonstrate our algorithm on a living cell.