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23 January 2019 Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry
Ingemar Fredriksson, Martin Hultman, Tomas Strömberg, Marcus Larsson
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Journal of Biomedical Optics, Vol. 24, Issue 1, 016001 (January 2019). https://doi.org/10.1117/1.JBO.24.1.016001
Abstract
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Ingemar Fredriksson, Martin Hultman, Tomas Strömberg, and Marcus Larsson "Machine learning in multiexposure laser speckle contrast imaging can replace conventional laser Doppler flowmetry," Journal of Biomedical Optics 24(1), 016001 (23 January 2019). https://doi.org/10.1117/1.JBO.24.1.016001
Received: 21 September 2018; Accepted: 17 December 2018; Published: 23 January 2019
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Cited by 22 scholarly publications and 1 patent.
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KEYWORDS
Laser speckle contrast imaging
Doppler effect
Data modeling
Speckle
Stochastic processes
Machine learning
In vivo imaging
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