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

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:...

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Autores principales: Marotz, Jörg, Kulcke, Axel, Siemers, Frank, Cruz, Diogo, Aljowder, Ahmed, Promny, Dominik, Daeschlein, Georg, Wild, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891704/
https://www.ncbi.nlm.nih.gov/pubmed/31744187
http://dx.doi.org/10.3390/molecules24224164
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author Marotz, Jörg
Kulcke, Axel
Siemers, Frank
Cruz, Diogo
Aljowder, Ahmed
Promny, Dominik
Daeschlein, Georg
Wild, Thomas
author_facet Marotz, Jörg
Kulcke, Axel
Siemers, Frank
Cruz, Diogo
Aljowder, Ahmed
Promny, Dominik
Daeschlein, Georg
Wild, Thomas
author_sort Marotz, Jörg
collection PubMed
description 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.
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spelling pubmed-68917042019-12-12 Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine Marotz, Jörg Kulcke, Axel Siemers, Frank Cruz, Diogo Aljowder, Ahmed Promny, Dominik Daeschlein, Georg Wild, Thomas Molecules Article 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. MDPI 2019-11-17 /pmc/articles/PMC6891704/ /pubmed/31744187 http://dx.doi.org/10.3390/molecules24224164 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Marotz, Jörg
Kulcke, Axel
Siemers, Frank
Cruz, Diogo
Aljowder, Ahmed
Promny, Dominik
Daeschlein, Georg
Wild, Thomas
Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title_full Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title_fullStr Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title_full_unstemmed Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title_short Extended Perfusion Parameter Estimation from Hyperspectral Imaging Data for Bedside Diagnostic in Medicine
title_sort extended perfusion parameter estimation from hyperspectral imaging data for bedside diagnostic in medicine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6891704/
https://www.ncbi.nlm.nih.gov/pubmed/31744187
http://dx.doi.org/10.3390/molecules24224164
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