Split Bregman迭代算法生物发光断层成像

生物发光断层成像重建中,发光光源在生物体内稀疏分布,基于压缩感知思想,将?1范数正则化的稀疏重建应用于生物发光断层成像,并采用Split Bregman迭代算法求解?1范数目标函数,以获得快速、稳定的重建.三维数字鼠模型数值实验结果表明,该算法应用于生物发光断层成像重建,在没有使用任何光源可行区域先验和多光谱测量信息的条件下,仍能获得准确的定位和定量重建结果,算法对噪声具有较好的鲁棒性.

The Split Bregman iteration algorithm for bioluminescence tomography

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 gl-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-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.