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首页> 外文会议>BMEI 2012;International Conference on Biomedical Engineering and Informatics >Research on mouse tissue classification in bioluminescence tomography forward problem
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Research on mouse tissue classification in bioluminescence tomography forward problem

机译：生物发光层析成像正向问题中的小鼠组织分类研究

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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.
机译：小鼠模型是生物发光层析成像正向问题研究中最常用的动物模型之一。老鼠的CT（计算机断层扫描）和MRI（磁共振成像）图像分割的准确性将直接影响结点的数量和网格的质量。本文着眼于不同组织分类对正向问题的影响。我们首先使用有限元方法（FEM）解决了模型上的正向问题，然后使用COMSOL Multiphysics和MMCM（基于网格的蒙特卡洛方法）来证明有限元方法的可行性。最后，我们将数字鼠标的组织分为不同的组，并通过仿真结果找出不同的鼠标组织分类对正向问题的影响。仿真结果将为小鼠CT和MRI图像的分割提供参考，并为简化鼠标数据的网格化提供基础。

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