Split Bregman迭代算法生物发光断层成像
摘要
In bioluminescence tomography applications, the spatial distribution of the interior bioluminescent light source is typically sparse in the imaging domain. Inspired by the compressive sensing recovery, a sparse reconstruction scheme based on l 1-norm regularization is presented for bioluminescence tomography. To obtain stable and fast reconstruction result, the Split Bregman iteration algorithm is employed to solve the l 1-norm regularized objective function. Numerical experiment results with a digital mouse model illustrate that the proposed method can accurately localize and quantify source distribution without either permissible source region constraint or multispectral measurements. The experimental results also show that the proposed algorithm has good robustness and ideal stability.