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Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering. Perimed AB, Sweden.ORCID iD: 0000-0002-3454-6576
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Biomedical Engineering, Division of Biomedical Engineering. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0001-6385-6760
2019 (English)In: Journal of Biomedical Optics, ISSN 1083-3668, E-ISSN 1560-2281, Vol. 24, no 1, article id 016001Article in journal (Refereed) Published
Abstract [en]

Laser speckle contrast imaging (LSCI) enables video rate imaging of blood flow. However, its relation to tissue blood perfusion is nonlinear and depends strongly on exposure time. By contrast, the perfusion estimate from the slower laser Doppler flowmetry (LDF) technique has a relationship to blood perfusion that is better understood. Multiexposure LSCI (MELSCI) enables a perfusion estimate closer to the actual perfusion than that using a single exposure time. We present and evaluate a method that utilizes contrasts from seven exposure times between 1 and 64 ms to calculate a perfusion estimate that resembles the perfusion estimate from LDF. The method is based on artificial neural networks (ANN) for fast and accurate processing of MELSCI contrasts to perfusion. The networks are trained using modeling of Doppler histograms and speckle contrasts from tissue models. The importance of accounting for noise is demonstrated. Results show that by using ANN, MELSCI data can be processed to LDF perfusion with high accuracy, with a correlation coefficient R = 1.000 for noise-free data, R = 0.993 when a moderate degree of noise is present, and R = 0.995 for in vivo data from an occlusion-release experiment. (C) The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License.

Place, publisher, year, edition, pages
SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS , 2019. Vol. 24, no 1, article id 016001
Keywords [en]
blood flow; microcirculation; laser speckle contrast analysis; artificial neural networks
National Category
Medical Laboratory and Measurements Technologies
Identifiers
URN: urn:nbn:se:liu:diva-156587DOI: 10.1117/1.JBO.24.1.016001ISI: 000463884000006PubMedID: 30675771OAI: oai:DiVA.org:liu-156587DiVA, id: diva2:1315629
Note

Funding Agencies|Swedish Research Council [2014-6141]; Swedens Innovation Agency VINNOVA [2016-02211, 2017-01435]

Available from: 2019-05-14 Created: 2019-05-14 Last updated: 2019-10-22

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Fredriksson, IngemarHultman, MartinStrömberg, TomasLarsson, Marcus
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