A Graph-guided Hybrid Regularization Method For Bioluminescence Tomography.

• The graph-guided-sparsity-inducing penalty can strengthen the bioluminescent energy spatial aggregation and relation, since it reflects not only the non-linear inverse relationship between bioluminescent energy deviation and their distance for any two nodes, but also the connection between the local bioluminescent energy clusters consisting of multiple the nearest adjacent nodes. • The GGHR method based on three constraints (sparsity, smoothness, graph-guided penalty) can better balance the sparsity, smoothness and morphological characteristics of the bioluminescence targets. • Dual decomposition and Nesterov's smoothing technique are used to provide an efficient optimization approach for solving the non-separable and non-smooth constrain problem. • The GGHR method can effectively alleviate the ill-posed inverse problem. • The GGHR method performs well in spatial location, morphology and bioluminescent energy recovery and preclinical practicality. Background and objective: Bioluminescence tomography (BLT) is a powerful and sensitive imaging technique having great potential in preclinical application, such as tumor imaging, monitoring and therapy, etc. Regularization plays an important role in BLT reconstruction for considering the priori information to overcome the inherent ill-posedness of the inverse problem. Therefore, well-designed regularization term and sophisticated algorithm for solving the consequent optimization problem are key to improve the BLT quality. Methods: To balance the sparsity, smoothness and morphological characteristics of the bioluminescence targets, we constructed a novel Graph-Guided Hybrid Regularization (GGHR) method by combining graph-guided penalty term with L 1 and L 2 norm regularizer. To solve the corresponding minimization problem with hybrid penalties, the dual decomposition and Nesterov's smoothing technique were adopted to decouple and transform the non-separable and non-smooth graph-guided penalty term into a differential smooth approximation form, which was solved by the fast iterative shrinkage thresholding algorithm. Results: The performance of the proposed GGHR method was verified and evaluated through a series of simulation, phantom and in vivo experiments. The comparison results demonstrated that the GGHR method outperformed current mainstream reconstruction algorithms in spatial localization, morphology recovery and in vivo practicality. Conclusions: The proposed GGHR method is a robust and practicality reconstruction algorithm for further highlighting the positive effect of hybrid regularization on BLT applications. [ABSTRACT FROM AUTHOR]