Machine learning estimation of tissue optical properties

Abstract Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal resp...

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Autores principales:	Brett H. Hokr, Joel N. Bixler
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/cad84211eeee413db0d3df40825ec13e
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spelling	oai:doaj.org-article:cad84211eeee413db0d3df40825ec13e2021-12-02T17:04:35ZMachine learning estimation of tissue optical properties10.1038/s41598-021-85994-w2045-2322https://doaj.org/article/cad84211eeee413db0d3df40825ec13e2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85994-whttps://doaj.org/toc/2045-2322Abstract Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.Brett H. HokrJoel N. BixlerNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-7 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Brett H. Hokr Joel N. Bixler Machine learning estimation of tissue optical properties
description	Abstract Dynamic, in vivo measurement of the optical properties of biological tissues is still an elusive and critically important problem. Here we develop a technique for inverting a Monte Carlo simulation to extract tissue optical properties from the statistical moments of the spatio-temporal response of the tissue by training a 5-layer fully connected neural network. We demonstrate the accuracy of the method across a very wide parameter space on a single homogeneous layer tissue model and demonstrate that the method is insensitive to parameter selection of the neural network model itself. Finally, we propose an experimental setup capable of measuring the required information in real time in an in vivo environment and demonstrate proof-of-concept level experimental results.
format	article
author	Brett H. Hokr Joel N. Bixler
author_facet	Brett H. Hokr Joel N. Bixler
author_sort	Brett H. Hokr
title	Machine learning estimation of tissue optical properties
title_short	Machine learning estimation of tissue optical properties
title_full	Machine learning estimation of tissue optical properties
title_fullStr	Machine learning estimation of tissue optical properties
title_full_unstemmed	Machine learning estimation of tissue optical properties
title_sort	machine learning estimation of tissue optical properties
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/cad84211eeee413db0d3df40825ec13e
work_keys_str_mv	AT bretthhokr machinelearningestimationoftissueopticalproperties AT joelnbixler machinelearningestimationoftissueopticalproperties
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Machine learning estimation of tissue optical properties

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