The inverse problem of fluorescence molecular tomography is seriously ill-posed and very sensitive to noise. In order to improve the accuracy of targeted fluorophorea reconstruction method is proposed combining a low rank matrix completion algorithm with manifold regularization to suppress noise. More priori information is utilized to improve the reconstruction results of inverse problem. In the proposed methodboth the low-rank attribute of the observation matrix and the target aggregation of the reconstructed light source were used to improve the accuracy of the inverse problem reconstruction. The low rank matrix completion algorithm was employed to suppress the background fluorescence; the manifold regular model was employed to get a more aggregate and accurate solution. Finallyaccuracy of the reconstruction results were improved by this mean. To validate the proposed methodnumerical experiments were conducted. The experiment results demonstrated that the inverse problem reconstruction results were improved greatly in aggregation and accuracy.