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Open AccessArticle

Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine

Klinik für Plastische und Handchirurgie und Brandverletztenzentrum, BG-Klinikum Bergmannstrost, D-06002 Halle (Saale), Germany
Institute of Applied Bioscience and Process Management, University of Applied Science Anhalt, D-06366 Köthen (Anhalt), Germany
Diaspective Vision GmbH, D-18233 Am Salzhaff, Germany
Clinic of Plastic, Hand and Aesthetic Surgery, Medical Center Dessau, University of Applied Science Anhalt, D-06847 Dessau, Germany
Clinic of Dermatology, Immunology and Allergology, Medical Center Dessau, Medical University Brandenburg “Theodor Fontane“ Medical Center Dessau, D-06847 Dessau, Germany
Klinik für Plastische, Wiederherstellende und Handchirurgie, Zentrum für Schwerbrandverletzte, Klinikum Nürnberg, D-90471 Nürnberg, Germany
Klinik und Poliklinik für Hautkrankheiten, Universitätsmedizin Greifswald, D-17475 Greifswald, Germany
Author to whom correspondence should be addressed.
Academic Editors: Christian Huck and Krzysztof B. Bec
Molecules 2019, 24(22), 4164;
Received: 12 September 2019 / Revised: 30 October 2019 / Accepted: 14 November 2019 / Published: 17 November 2019
Background: Hyperspectral Imaging (HSI) has a strong potential to be established as a new contact-free measuring method in medicine. Hyperspectral cameras and data processing have to fulfill requirements concerning practicability and validity to be integrated in clinical routine processes. Methods: Calculating physiological parameters which are of significant clinical value from recorded remission spectra is a complex challenge. We present a data processing method for HSI remission spectra based on a five-layer model of perfused tissue that generates perfusion parameters for every layer and presents them as depth profiles. The modeling of the radiation transport and the solution of the inverse problem are based on familiar approximations, but use partially heuristic methods for efficiency and to fulfill practical clinical requirements. Results: The parameter determination process is consistent, as the measured spectrum is practically completely reproducible by the modeling sequence; in other words, the whole spectral information is transformed into model parameters which are easily accessible for physiological interpretation. The method is flexible enough to be applicable on a wide spectrum of skin and wounds. Examples of advanced procedures utilizing extended perfusion representation in clinical application areas (flap control, burn diagnosis) are presented. View Full-Text
Keywords: hyperspectral image processing; perfusion measurements; clinical classifications hyperspectral image processing; perfusion measurements; clinical classifications
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Marotz, J.; Kulcke, A.; Siemers, F.; Cruz, D.; Aljowder, A.; Promny, D.; Daeschlein, G.; Wild, T. Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine. Molecules 2019, 24, 4164.

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