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A predictive model of backscattering at subdiffusion length scales

We provide a methodology for accurately predicting elastic backscattering radial distributions from random media with two simple empirical models. We apply these models to predict the backscattering based on two classes of scattering phase functions: the Henyey-Greenstein phase function and a genera...

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Detalles Bibliográficos
Autores principales: Turzhitsky, Vladimir, Radosevich, Andrew, Rogers, Jeremy D., Taflove, Allen, Backman, Vadim
Formato: Texto
Lenguaje:English
Publicado: Optical Society of America 2010
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018048/
https://www.ncbi.nlm.nih.gov/pubmed/21258528
http://dx.doi.org/10.1364/BOE.1.001034
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author Turzhitsky, Vladimir
Radosevich, Andrew
Rogers, Jeremy D.
Taflove, Allen
Backman, Vadim
author_facet Turzhitsky, Vladimir
Radosevich, Andrew
Rogers, Jeremy D.
Taflove, Allen
Backman, Vadim
author_sort Turzhitsky, Vladimir
collection PubMed
description We provide a methodology for accurately predicting elastic backscattering radial distributions from random media with two simple empirical models. We apply these models to predict the backscattering based on two classes of scattering phase functions: the Henyey-Greenstein phase function and a generalized two parameter phase function that is derived from the Whittle-Matérn correlation function. We demonstrate that the model has excellent agreement over all length scales and has less than 1% error for backscattering at subdiffusion length scales for tissue-relevant optical properties. The presented model is the first available approach for accurately predicting backscattering at length scales significantly smaller than the transport mean free path.
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spelling pubmed-30180482011-01-21 A predictive model of backscattering at subdiffusion length scales Turzhitsky, Vladimir Radosevich, Andrew Rogers, Jeremy D. Taflove, Allen Backman, Vadim Biomed Opt Express Optics of Tissue and Turbid Media We provide a methodology for accurately predicting elastic backscattering radial distributions from random media with two simple empirical models. We apply these models to predict the backscattering based on two classes of scattering phase functions: the Henyey-Greenstein phase function and a generalized two parameter phase function that is derived from the Whittle-Matérn correlation function. We demonstrate that the model has excellent agreement over all length scales and has less than 1% error for backscattering at subdiffusion length scales for tissue-relevant optical properties. The presented model is the first available approach for accurately predicting backscattering at length scales significantly smaller than the transport mean free path. Optical Society of America 2010-09-30 /pmc/articles/PMC3018048/ /pubmed/21258528 http://dx.doi.org/10.1364/BOE.1.001034 Text en ©2010 Optical Society of America http://creativecommons.org/licenses/by-nc-nd/3.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 Unported License, which permits download and redistribution, provided that the original work is properly cited. This license restricts the article from being modified or used commercially.
spellingShingle Optics of Tissue and Turbid Media
Turzhitsky, Vladimir
Radosevich, Andrew
Rogers, Jeremy D.
Taflove, Allen
Backman, Vadim
A predictive model of backscattering at subdiffusion length scales
title A predictive model of backscattering at subdiffusion length scales
title_full A predictive model of backscattering at subdiffusion length scales
title_fullStr A predictive model of backscattering at subdiffusion length scales
title_full_unstemmed A predictive model of backscattering at subdiffusion length scales
title_short A predictive model of backscattering at subdiffusion length scales
title_sort predictive model of backscattering at subdiffusion length scales
topic Optics of Tissue and Turbid Media
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3018048/
https://www.ncbi.nlm.nih.gov/pubmed/21258528
http://dx.doi.org/10.1364/BOE.1.001034
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