Application of diffusion approximation in quantitative photoacoustic tomography in the presence of low-scattering regions. (July 2020)

• Record Type: Journal Article
• Authors: Hänninen, Niko; Pulkkinen, Aki; Leino, Aleksi; Tarvainen, Tanja
• Is Part Of: Journal of quantitative spectroscopy & radiative transfer. Volume 250(2020)
• Publication Date: 2020-07
• Subjects: Inverse problems -- Quantitative photoacoustic tomography -- Uncertainty quantification -- Bayesian methods -- Model reduction -- Bayesian approximation error modeling; Spectrum analysis -- Periodicals; Radiation -- Periodicals; Analyse spectrale -- Périodiques; Rayonnement -- Périodiques; Radiation; Spectrum analysis; Periodicals; 543.0858
• DOI: 10.1016/j.jqsrt.2020.107065
• Languages: English
• ISSNs: 0022-4073

Abstract

Highlights: Bayesian framework enables accurate estimates and reliability assessment. Modeling errors can be compensated using Bayesian methods. Modeling of the errors improves the reliability of the credibility estimates. 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.