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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,...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2023
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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. |
format | Online Article Text |
id | pubmed-10027009 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
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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