We present a sparse Bayesian reconstruction method based on multiple types of a priori information for multispectral bioluminescence tomography (BLT). In the Bayesian approach, five kinds of a priori information are incorporated, reducing the ill-posedness of BLT. Specifically, source sparsity characteristic is considered to promote reconstruction results. Considering the computational burden in the multispectral case, a series of strategies is adopted to improve computational efficiency, such as optimal permissible source region strategy and node model of the finite element method. The performance of the proposed algorithm is validated by a heterogeneous three-dimensional (3D) micron scale computed tomography atlas and a mouse-shaped phantom. Reconstructed results demonstrate the feasibility and effectiveness of the proposed algorithm.

To avoid the ill-posedness in the inverse problem of bioluminescence tomography, a moment searching algorithm fusing the finite element method (FEM) with the moment concept in theoretical mechanics is developed. In the algorithm, the source's information is mapped to the surface photon flux density by FEM, and the source's position is modified with the feedback through the algorithm of barycenter searching, which makes full use of the position information of the photon flux density on surface. The position is modified in every iterative step and will finally converge to the real source's value theoretically.