Compensating modeling errors of diffusion approximation in quantitative photoacoustic tomography using a Bayesian approach

Abstract

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.