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Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models
Significance: Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantita...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Society of Photo-Optical Instrumentation Engineers
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426481/ https://www.ncbi.nlm.nih.gov/pubmed/32798354 http://dx.doi.org/10.1117/1.JBO.25.8.085002 |
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author | Macdonald, Callum M. Arridge, Simon Powell, Samuel |
author_facet | Macdonald, Callum M. Arridge, Simon Powell, Samuel |
author_sort | Macdonald, Callum M. |
collection | PubMed |
description | Significance: Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. Aim: Our aim is to enable computationally efficient image reconstruction in (hybrid) diffuse optical modalities using stochastic forward models. Approach: Using Monte Carlo, we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community, we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme to substantially reduce computational resources at each step. Results: For example problems of quantitative photoacoustic tomography and ultrasound-modulated optical tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high-accuracy forward run of the same Monte Carlo model. Conclusions: This approach demonstrates significant computational savings when approaching the full nonlinear inverse problem of optical property estimation using stochastic methods. |
format | Online Article Text |
id | pubmed-7426481 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Society of Photo-Optical Instrumentation Engineers |
record_format | MEDLINE/PubMed |
spelling | pubmed-74264812020-08-18 Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models Macdonald, Callum M. Arridge, Simon Powell, Samuel J Biomed Opt General Significance: Indirect imaging problems in biomedical optics generally require repeated evaluation of forward models of radiative transport, for which Monte Carlo is accurate yet computationally costly. We develop an approach to reduce this bottleneck, which has significant implications for quantitative tomographic imaging in a variety of medical and industrial applications. Aim: Our aim is to enable computationally efficient image reconstruction in (hybrid) diffuse optical modalities using stochastic forward models. Approach: Using Monte Carlo, we compute a fully stochastic gradient of an objective function for a given imaging problem. Leveraging techniques from the machine learning community, we then adaptively control the accuracy of this gradient throughout the iterative inversion scheme to substantially reduce computational resources at each step. Results: For example problems of quantitative photoacoustic tomography and ultrasound-modulated optical tomography, we demonstrate that solutions are attainable using a total computational expense that is comparable to (or less than) that which is required for a single high-accuracy forward run of the same Monte Carlo model. Conclusions: This approach demonstrates significant computational savings when approaching the full nonlinear inverse problem of optical property estimation using stochastic methods. Society of Photo-Optical Instrumentation Engineers 2020-08-14 2020-08 /pmc/articles/PMC7426481/ /pubmed/32798354 http://dx.doi.org/10.1117/1.JBO.25.8.085002 Text en © 2020 The Authors https://creativecommons.org/licenses/by/4.0/ 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. |
spellingShingle | General Macdonald, Callum M. Arridge, Simon Powell, Samuel Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title | Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title_full | Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title_fullStr | Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title_full_unstemmed | Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title_short | Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models |
title_sort | efficient inversion strategies for estimating optical properties with monte carlo radiative transport models |
topic | General |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426481/ https://www.ncbi.nlm.nih.gov/pubmed/32798354 http://dx.doi.org/10.1117/1.JBO.25.8.085002 |
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