A Bayesian level-set inversion method for simultaneous reconstruction of absorption and diffusion coefficients in diffuse optical tomography

Anuj Abhishek, Thilo Strauss, Taufiquar Khan

Abstract: In this article, we propose a non-parametric Bayesian level-set method for simultaneous reconstruction of piecewise constant diffusion and absorption coefficients in diffuse optical tomography (DOT). We show that the Bayesian formulation of the corresponding inverse problem is well-posed and the posterior measure as a solution to the inverse problem satisfies a Lipschitz estimate with respect to the measured data in terms of Hellinger distance. We reduce the problem to a shape-reconstruction problem and use level-set priors for the parameters of interest. We demonstrate the efficacy of the proposed method using numerical simulations by performing reconstructions of the original phantom using two reconstruction methods. Posing the inverse problem in a Bayesian paradigm allows us to do statistical inference for the parameters of interest whereby we are able to quantify the uncertainty in the reconstructions for both the methods. This illustrates a key advantage of Bayesian methods over traditional algorithms.