Hybrid reconstruction framework for model-based multispectral bioluminescence tomography based on Alpha-divergence

Bioluminescence tomography(BLT)is a promising imaging modality that can provide noninvasive three-dimensional visualization information on tumor distribution.In BLT reconstruction,the widely used methods based on regularization or greedy strategy face problems such as over-sparsity,over-smoothing,spatial discontinuity,poor robustness,and poor multi-target resolution.To deal with these problems,combining the advantages of the greedy strategies as well as regularization methods,we propose a hybrid reconstruction framework for model-based multispectral BLT using the support set of a greedy strategy as a feasible region and the Alpha-divergence to combine the weighted solutions obtained by L1-norm and L2-norm regularization methods.In numerical simulations with digital mouse and in vivo experiments,the results show that the proposed framework has better localization accuracy,spatial resolution,and multi-target resolution.