Gradient Projection for Sparse Reconstruction Method for Dynamic Fluorescence Molecular Tomography

摘要

Dynamic fluorescence molecular tomography (FMT) is a promising optical imaging technique for three-dimensionally demonstrating the metabolic process of fluorophore in small animals. Conventional FMT methods focus on reconstructing static distribution of fluorescent yield, and the reconstruction results may perform poorly if the boundary measurement data, acquired from time-varying fluorophore, were directly used in these methods. In this study, we apply joint l_1 and Laplacian manifold regularization model to dynamic FMT. The l_1-norm regularization method is used to deal with the ill-posedness of FMT, and the Laplacian manifold regularization is introduced to obtain spatial structure information of boundary measurements. Then, we use gradient projection for sparse reconstruction (GPSR) method to solve the joint regularization model. Since the boundary measurements are obtained from different time points, the input data is converted from a vector to a matrix, and each column of the matrix corresponds to a time point. Thus, a sequence of fluorophore concentration images, corresponding to different time points, can be reconstructed in one step. Numerical simulation experiments are performed and the results indicate that the proposed method can recover the dynamic fluorophore well.