CASIA OpenIR  > 中国科学院分子影像重点实验室
Fast and Robust Reconstruction Method for Fluorescence Molecular Tomography based on Deep Neural Network
Huang C(黄超)1; Meng Hui1; Yuan Gao1; Shixin Jiang2; Kun Wang1; Jie Tian1
Conference NameSociety of Photo-Optical Instrumentation Engineers,Photonics West
Conference Date2019-02-02
Conference PlaceThe Moscone Center, San Francisco, California, USA

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.

KeywordFluorescence Molecular Tomography, Ill-poseness, Deep Convolution Neural Network, Reconstruction.
Indexed ByEI
Sub direction classification医学影像处理与分析
Document Type会议论文
Corresponding AuthorJie Tian
Recommended Citation
GB/T 7714
Huang C,Meng Hui,Yuan Gao,et al. Fast and Robust Reconstruction Method for Fluorescence Molecular Tomography based on Deep Neural Network[C],2019.
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