Optical Molecular Imaging (OMI) has become an important and rapidly emerging molecular imaging technology, and has been widely used in cancer detection, drug development and the visualization of gene expression. Based on the variety of fluorescent probes and its low cost, Fluorescent molecular imaging (FMI) has become a promising optical molecular imaging technology. However, due to the high-scattering
and low-absorption of near-infrared or visible-light photons, the two-dimensional FMI cannot accurately reflect true distribution of the probe. Therefore, Fluorescence Molecular Tomography (FMT) which can three-dimensionally reconstruct the distribution of fluorescent probe has been rapidly developed in theory and practical applications. FMT has great application potential and essential research value in the early detection of cancer, pathology research and the visualization of cells. However, the diffusion equations (DE) can not fit the transmission law of photon in heterogeneous biological tissues well and the ill-posedness of the inverse problem limit the reconstruction accuracy of FMT. Therefore, further exploration is needed in these two aspects.
In this thesis, we research on dealing with the problems with the powerful fitting ability of neural network. Besides, we also combined the forward problem and the inverse problem into one neural model. Then a new deep learning based method is proposed to improve the reconstruction efficiency and detailed verifications are performed by using numerical simulation experiments. The main work of the thesis includes the followings:
1. Research on the forward and inverse problems of FMT Light transmitting in biological tissues will undergo complex interactions such as absorption, scattering. In practical applications, RTE is generally used to establish the transmission model. However, the RTE is a complex calculus equation and the solution process is inefficient and requires significant computational resources. Considering the high scattering and low absorption characteristics of near-infrared light in biological
tissues, the diffusion equation (DE) can be used as an approximate model of the RTE. Since the DE is a typical elliptic partial differential equation, it can be transformed into a matrix-form target equation by the finite element method. In order to reduce the significant ill-posedness of the target equation, great amount of effects were made for optimization by adding sparse prior information or using greedy solving strategy.
2. Research on FMT reconstruction algorithm based on deep learning
In order to improve the reconstruction result of the FMT reconstruction, besides reducing the ill-conditionedness of the inverse problem, a high-order simplified spherical harmonics approximation (SPN) can also be used. Although these methods have gradually improved the reconstruction quality of FMT, they still have not achieved
satisfactory results for FMT reconstruction. In the conventional FMT reconstruction, the nonlinear RTE is extensively approximated by DE or SPN, which inevitably causes errors in FMT reconstruction. Therefore, we propose an FMT reconstruction algorithm based on deep learning, which can better reduce the difference between the approximate model and the radiation transfer model.
3. Numerical simulation experiment and in vivo experiments
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 our method has better performance than the conventional iterated shrinkage based method in tumor position locating. What’s more, the results
also revealed the good robustness for the varied tumor depth and organ distribution. Besides, in vivo mouse experiments was also carried out to verify the property of our method. To the best of our knowledge, this is the first study that employed deep learning method in FMT reconstruction and also the first study that combined the forward
problem and the inverse problem into one neural model, which holds a great potential of improving the reconstruction quality of FMT.