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.
© 2010 Chinese Optics Letters
(100.3010) Image processing : Image reconstruction techniques
(100.3190) Image processing : Inverse problems
(170.3010) Medical optics and biotechnology : Image reconstruction techniques
(170.6960) Medical optics and biotechnology : Tomography
Jinchao Feng, Kebin Jia, Chenghu Qin, Shouping Zhu, Xin Yang, and Jie Tian, "Sparse Bayesian reconstruction method for multispectral bioluminescence tomography," Chin. Opt. Lett. 8, 1010-1014 (2010)