Three-dimensional Bioluminescence Tomography based on Bayesian approach

Abstract

Bioluminescence tomography (BLT) poses a typical ill-posed inverse problem with a large number of unknowns and a relatively limited number of boundary measurements. It is indispensable to incorporate a priori information into the inverse problem formulation in order to obtain viable solutions. In the paper, Bayesian approach has been firstly suggested to incorporate multiple types of a priori information for BLT reconstruction. Meanwhile, a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of finite element analysis. Then the distribution of bioluminescent source can be acquired by maximizing the log posterior probability with respect to a noise parameter and the unknown source density. Furthermore, the use of finite element method makes the algorithm appropriate for complex heterogeneous phantom. The algorithm was validated by numerical simulation of a 3-D micro-CT mouse atlas and physical phantom experiment. The reconstructed results suggest that we are able to achieve high computational efficiency and accurate localization of bioluminescent source.