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Compressed Sensing for Optical Coherence Tomography Angiography Volume Generation

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Bildverarbeitung für die Medizin 2020

Part of the book series: Informatik aktuell ((INFORMAT))

Zusammenfassung

Optical coherence tomography angiography (OCTA) is an increasingly popular modality for imaging of the retinal vasculature. Repeated optical coherence tomography (OCT) scans of the retina allow the computation of motion contrast to display the retinal vasculature. To the best of our knowledge, we present the first application of compressed sensing for the generation of OCTA volumes. Using a probabilistic signal model for the computation of OCTA volumes and a 3D median filter, it is possible to perform compressed sensing reconstruction of OCTA volumes while suppressing noise. The presented approach was tested on a ground truth, averaged from ten individual OCTA volumes. Average reductions of the mean squared error of 9:67% were achieved when comparing reconstructed OCTA images to the stand-alone application of a 3D median filter.

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Literatur

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Author information

Authors and Affiliations

  1. Pattern Recognition Lab, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Deutschland

    Lennart Husvogt, Stefan B. Ploner, Daniel Stromer, Julia Schottenhamml & Andreas Maier

  2. Biomedical Optical Imaging and Biophotonics Group, MIT, Cambridge, USA

    Lennart Husvogt, Eric Moult & James G. Fujimoto

Authors
  1. Lennart Husvogt
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  2. Stefan B. Ploner
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  3. Daniel Stromer
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  4. Julia Schottenhamml
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  5. Eric Moult
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  6. James G. Fujimoto
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  7. Andreas Maier
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Corresponding author

Correspondence to Lennart Husvogt .

Editor information

Editors and Affiliations

  1. Institut für Medizinische Informatik, Charité - Universitätsmedizin Berlin, Berlin, Germany

    Thomas Tolxdorff

  2. Peter L. Reichertz Institut für Medizinische Informatik, Technische Universität Braunschweig, Braunschweig, Germany

    Thomas M. Deserno

  3. Institut für Medizinische Informatik, Universität zu Lübeck, Lübeck, Germany

    Heinz Handels

  4. Lehrstuhl für Mustererkennung, Friedrich-Alexander-Universität, Erlangen, Germany

    Andreas Maier

  5. Medical Image Computing, E230, Deutsches Krebsforschungszentrum (DKFZ), Heidelberg, Germany

    Klaus H. Maier-Hein

  6. Fakultät für Informatik und Mathematik, Ostbayerische Technische Hochschule Regensburg, Regensburg, Germany

    Christoph Palm

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© 2020 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature

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Husvogt, L. et al. (2020). Compressed Sensing for Optical Coherence Tomography Angiography Volume Generation. In: Tolxdorff, T., Deserno, T., Handels, H., Maier, A., Maier-Hein, K., Palm, C. (eds) Bildverarbeitung für die Medizin 2020. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-29267-6_19

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