Fast and robust reconstruction method for fluorescence molecular tomography based on deep neural network
Fluorescence molecular tomography (FMT) is a promising imaging technique in applications of preclinical research. However, the complexity of radiative transfer equation (RTE) and the ill poseness of the inverse problem limit the effectiveness of FMT reconstruction. In this research, we proposed a novel Deep Convolutional Neural Network (DCNN) Gated Recurrent Unit (GRU) and Multiple Layer Perception (MLP) based method (DGMM) for FMT reconstruction. Instead of establishing the photon transmission models and solving the inverse problem, the proposed method directly fits the nonlinear relationship between fluorescence intensity at the boundary and fluorescent source in biological tissue. For details, DGMM consists of three stages: In the first stage, the measured optical intensity was encoded into a feature vector by transferring the VGG16 model; In the second stage, we fused all encoded feature vectors into one feature vector by using GRU based network; In the last stage, the fused feature vector was used to reconstruct the fluorescent sources by MLP model. To evaluate the performance of our proposed method, a 3D digital mouse was utilized to generate FMT Monte Carlo simulation samples. In quantitative analysis, the results demonstrated that DGMM method has comparable performance with conventional method in tumor position locating. To the best of our knowledge, this is the first study that employed DCNN based methods for FMT reconstruction, which holds a great potential of improving the reconstruction quality of FMT.
BiOS