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.     

Voxel classification methodology for rapid Monte Carlo simulation of light propagation in complex media

Monte Carlo (MC) method is a statistical method for simulating photon propagation in media in the optical molecular imaging field.However,obtaining an accurate result using the method is quite time-consuming,especially because the boundary of the media is complex.A voxel classification method is proposed to reduce the computation cost.All the voxels generated by dividing the media are classified into three types (outside,boundary,and inside) according to the position of the voxel.The classified information is used to determine the relative position of the photon and the intersection between photon path and media boundary in the MC method.The influencing factors and effectiveness of the proposed method are analyzed and validated by simulation experiments.