Sparse Bayesian reconstruction method for multispectral bioluminescence tomography

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.