Abstract
Quantitative photoacoustic tomography is an imaging modality in which distributions of optical parameters inside tissue are estimated from photoacoustic images. This optical parameter estimation is an ill-posed problem and it needs to be approached in the framework of inverse problems. In this work, utilising surface light measurements in quantitative photoacoustic tomography is studied. Estimation of absorption and scattering is formulated as a minimisation problem utilising both internal quantitative photoacoustic data and surface light data. The image reconstruction problem is studied with two-dimensional numerical simulations in various imaging situations using the diffusion approximation as the model for light propagation. The results show that quantitative photoacoustic tomography augmented with surface light data can improve both absorption and scattering estimates when compared to the conventional quantitative photoacoustic tomography.
Highlights
Photoacoustic tomography (PAT) is an imaging modality based on the photoacoustic effect generated through the absorption of an externally introduced light pulse
The results show that quantitative photoacoustic tomography augmented with surface light data can improve both absorption and scattering estimates when compared to the conventional quantitative photoacoustic tomography
The Quantitative photoacoustic tomography (QPAT) image reconstruction problem was formulated as a minimisation problem in which absorption and scattering distributions were reconstructed utilising both QPAT and surface light data
Summary
Photoacoustic tomography (PAT) is an imaging modality based on the photoacoustic effect generated through the absorption of an externally introduced light pulse. Quantitative photoacoustic tomography (QPAT) is a technique which aims at estimating the absolute concentrations of the chromophores from photoacoustic images, i.e. from the reconstructed initial pressure distribution [9] This is an ill-posed problem which needs to be approached in the framework of inverse problems. In [43, 44], a two-step approach was suggested in which scattering distribution was first solved using diffuse optical tomography (DOT) measurements, and this information was utilised in the estimation of the absorbed optical energy density in photoacoustic imaging. In this work, estimating optical parameters using both absorbed optical energy density and surface light measurements is considered In the approach, these two data sources are utilised simultaneously in order to solve the inverse problem of QPAT in a Bayesian framework.
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