Niko Hanninen, A. Pulkkinen, A. Leino, T. Tarvainen
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Compensating modeling errors of diffusion approximation in quantitative photoacoustic tomography using a Bayesian approach
In this work, the inverse problem problem of quantitative photoacoustic tomography is approached in a Bayesian framework. Modeling errors caused by an approximative light transport model are compensated by utilizing Bayesian approximation error modeling.
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