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Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models.

Macdonald, C; Arridge, S; Powell, S; (2020) Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models. Journal of Biomedical Optics , 25 (8) , Article 085002. 10.1117/1.JBO.25.8.085002. Green open access

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Abstract

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.

Type: Article
Title: Efficient inversion strategies for estimating optical properties with Monte Carlo radiative transport models.
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/1.JBO.25.8.085002
Publisher version: https://doi.org/10.1117/1.JBO.25.8.085002
Language: English
Additional information: © The Authors. 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.
Keywords: Monte Carlo, machine learning, optical tomography, radiative transport, stochastic-gradient descent
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10109131
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