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

Hänninen, N; Pulkkinen, A; Leino, A; Tarvainen, T; (2020) Application of diffusion approximation in quantitative photoacoustic tomography in the presence of low-scattering regions. Journal of Quantitative Spectroscopy and Radiative Transfer , 250 10.1016/j.jqsrt.2020.107065. (In press).

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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.

Type: Article
Title: Application of diffusion approximation in quantitative photoacoustic tomography in the presence of low-scattering regions
DOI: 10.1016/j.jqsrt.2020.107065
Publisher version: http://dx.doi.org/10.1016/j.jqsrt.2020.107065
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Inverse problems, Quantitative photoacoustic tomography, Uncertainty quantification, Bayesian methods, Model reduction, Bayesian approximation error modeling
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
URI: https://discovery.ucl.ac.uk/id/eprint/10100200
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