增加测量信息可以有效降低荧光分子断层成像(FMT)重建的病态性，但随着数据增多，重建耗时也会显著增加。为了降低FMT重建的病态性和提升大规模数据集下的重建效率，结合对偶坐标下降法(DCA)和交替方向乘子法(ADMM)提出了一种改进的随机变量的交替方向乘子法重建优化方法。在原始ADMM方法的基础上，增加了一个随机更新规则，在每次迭代中只需要一个或者几个样本，就可加速收敛，使目标函数快速得到最优解，从而达到快速重建的效果。设计了数字鼠仿真实验和真实鼠实验，实验结果表明，所提方法在保证FMT重建图像精度的同时，显著提高了重建效率。

Increasing measurement information can effectively reduce the ill-posedness of the fluorescence molecular tomography(FMT) reconstruction. With the increase of data, the time of FMT reconstruction will increase significantly. In order to reduce the ill-posedness of FMT reconstruction and enhance reconstruction efficiency under big data sets, a reconstruction method of improved stochastic variant of alternating direction method of multipliers (ADMM) is proposed by combining dual coordinate ascent (DCA) method and ADMM method. The proposed method offers a stochastic update rule base on the original ADMM method where each iteration requires only one or few sample observations, thus gives speed up of convergence, so that the objective function can get the optimal solution rapidly and achieve the effect of rapid reconstruction. Simulation experiments of digital mouse and real mouse experiment show that the proposed method can guarantee FWT reconstruction images′s accuracy and improve reconstruction efficiency.