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Application of diffusion approximation in quantitative photoacoustic tomography in the presence of low-scattering regions

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Item embargoed until 2022-05-06. Restrictions imposed by the publisher
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Date
2020
Author(s)
Hänninen, Niko
Pulkkinen, Aki
Leino, Aleksi
Tarvainen, Tanja
Unique identifier
10.1016/j.jqsrt.2020.107065
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Citation
Hänninen, Niko. Pulkkinen, Aki. Leino, Aleksi. Tarvainen, Tanja. (2020). Application of diffusion approximation in quantitative photoacoustic tomography in the presence of low-scattering regions.  Journal of quantitative spectroscopy and radiative transfer, 250, 107065. 10.1016/j.jqsrt.2020.107065.
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©2020ElsevierLtd.
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CC BY-NC-ND https://creativecommons.org/licenses/by-nc-nd/4.0/
Abstract

In quantitative photoacoustic tomography, the aim is to reconstruct distributions of optical parameters of an imaged target from an initial pressure distribution obtained from ultrasound measurements. In order to obtain accurate and quantitative information on the optical parameters, modeling light transport in the target is required. Utilizing an approximative model for light transport would be favorable to reduce the computational cost, but the modeling errors of the approximative model can result in significant errors in the reconstructions. In this work, we approach the image reconstruction problem of quantitative photoacoustic tomography in the Bayesian framework. We utilize the Bayesian approximation error method to compensate for the modeling errors between the diffusion approximation and Monte Carlo model for light transport. The approach is studied with two-dimensional numerical simulations with varying optical parameters and noise levels. The results show that Bayesian approximation error method can be used to reduce the effects of the modeling errors in quantitative photoacoustic tomography in a wide range of optical parameters.

Subjects
inverse problems   quantitative photoacoustic tomography   uncertainty quantification   bayesian methods   model reduction   bayesian approximation error modeling   
URI
https://erepo.uef.fi/handle/123456789/8140
Link to the original item
http://dx.doi.org/10.1016/j.jqsrt.2020.107065
Publisher
Elsevier BV
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  • Luonnontieteiden ja metsätieteiden tiedekunta [1109]
University of Eastern Finland
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