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Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography

Leino, A; Lunttila, T; Mozumder, M; Pulkkinen, A; Tarvainen, T; (2020) Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography. IEEE Transactions on Medical Imaging 10.1109/tmi.2020.2983129. Green open access

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Abstract

Quantitative photoacoustic tomography aims at estimating optical parameters from photoacoustic images that are formed utilizing the photoacoustic effect caused by the absorption of an externally introduced light pulse. This optical parameter estimation is an ill-posed inverse problem, and thus it is sensitive to measurement and modeling errors. In this work, we propose a novel way to solve the inverse problem of quantitative photoacoustic tomography based on the perturbation Monte Carlo method. Monte Carlo method for light propagation is a stochastic approach for simulating photon trajectories in a medium with scattering particles. It is widely accepted as an accurate method to simulate light propagation in tissues. Furthermore, it is numerically robust and easy to implement. Perturbation Monte Carlo maintains this robustness and enables forming gradients for the solution of the inverse problem. We validate the method and apply it in the framework of Bayesian inverse problems. The simulations show that the perturbation Monte Carlo method can be used to estimate spatial distributions of both absorption and scattering parameters simultaneously. These estimates are qualitatively good and quantitatively accurate also in parameter scales that are realistic for biological tissues.

Type: Article
Title: Perturbation Monte Carlo Method for Quantitative Photoacoustic Tomography
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/tmi.2020.2983129
Publisher version: https://doi.org/10.1109/tmi.2020.2983129
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Quantitative photoacoustic tomography , perturbation Monte Carlo , inverse problems , photoacoustic imaging , optoacoustic imaging , image reconstruction
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/10098131
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