Research on mouse tissue classification in bioluminescence tomography forward problem

Mouse model is one of the most often used animal models in the research of forward problem of bioluminescence tomography. The accuracy of mice's CT (Computed Tomography) and MRI (Magnetic Resonance Imaging) images segmentation will directly influence the number of nodes and the quality of mesh. This paper focuses on the different tissue classifications' effects on the forward problem. We firstly use the finite element method (FEM) to solve the forward problem on phantom, and then use COMSOL Multiphysics and MMCM (Mesh-based Monte Carlo Method) to testify the feasibility of FEM. Finally, we classify the tissues of digital mouse into different groups and find out different mouse tissues classifications' effects on the forward problem through the simulation results. The simulation results will provide references for the segmentation of mice's CT and MRI images, and the basis for the mesh simplification of the mouse data.