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Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning

SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts,...

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Autores principales: Hultman, Martin, Larsson, Marcus, Strömberg, Tomas, Fredriksson, Ingemar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Society of Photo-Optical Instrumentation Engineers 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027009/
https://www.ncbi.nlm.nih.gov/pubmed/36950019
http://dx.doi.org/10.1117/1.JBO.28.3.036007
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author Hultman, Martin
Larsson, Marcus
Strömberg, Tomas
Fredriksson, Ingemar
author_facet Hultman, Martin
Larsson, Marcus
Strömberg, Tomas
Fredriksson, Ingemar
author_sort Hultman, Martin
collection PubMed
description SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality. AIM: To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. APPROACH: An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. RESULTS: Speed-resolved perfusion was estimated in the three speed intervals 0 to [Formula: see text] , 1 to [Formula: see text] , and [Formula: see text] , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. CONCLUSIONS: The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique.
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spelling pubmed-100270092023-03-21 Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning Hultman, Martin Larsson, Marcus Strömberg, Tomas Fredriksson, Ingemar J Biomed Opt Imaging SIGNIFICANCE: Laser speckle contrast imaging (LSCI) gives a relative measure of microcirculatory perfusion. However, due to the limited information in single-exposure LSCI, models are inaccurate for skin tissue due to complex effects from e.g. static and dynamic scatterers, multiple Doppler shifts, and the speed-distribution of blood. It has been demonstrated how to account for these effects in laser Doppler flowmetry (LDF) using inverse Monte Carlo (MC) algorithms. This allows for a speed-resolved perfusion measure in absolute units %RBC × mm/s, improving the physiological interpretation of the data. Until now, this has been limited to a single-point LDF technique but recent advances in multi-exposure LSCI (MELSCI) enable the analysis in an imaging modality. AIM: To present a method for speed-resolved perfusion imaging in absolute units %RBC × mm/s, computed from multi-exposure speckle contrast images. APPROACH: An artificial neural network (ANN) was trained on a large simulated dataset of multi-exposure contrast values and corresponding speed-resolved perfusion. The dataset was generated using MC simulations of photon transport in randomized skin models covering a wide range of physiologically relevant geometrical and optical tissue properties. The ANN was evaluated on in vivo data sets captured during an occlusion provocation. RESULTS: Speed-resolved perfusion was estimated in the three speed intervals 0 to [Formula: see text] , 1 to [Formula: see text] , and [Formula: see text] , with relative errors 9.8%, 12%, and 19%, respectively. The perfusion had a linear response to changes in both blood tissue fraction and blood flow speed and was less affected by tissue properties compared with single-exposure LSCI. The image quality was subjectively higher compared with LSCI, revealing previously unseen macro- and microvascular structures. CONCLUSIONS: The ANN, trained on modeled data, calculates speed-resolved perfusion in absolute units from multi-exposure speckle contrast. This method facilitates the physiological interpretation of measurements using MELSCI and may increase the clinical impact of the technique. Society of Photo-Optical Instrumentation Engineers 2023-03-20 2023-03 /pmc/articles/PMC10027009/ /pubmed/36950019 http://dx.doi.org/10.1117/1.JBO.28.3.036007 Text en © 2023 The Authors https://creativecommons.org/licenses/by/4.0/Published by SPIE under a Creative Commons Attribution 4.0 International License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
spellingShingle Imaging
Hultman, Martin
Larsson, Marcus
Strömberg, Tomas
Fredriksson, Ingemar
Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title_full Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title_fullStr Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title_full_unstemmed Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title_short Speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
title_sort speed-resolved perfusion imaging using multi-exposure laser speckle contrast imaging and machine learning
topic Imaging
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10027009/
https://www.ncbi.nlm.nih.gov/pubmed/36950019
http://dx.doi.org/10.1117/1.JBO.28.3.036007
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