A Finite Element Mesh Regrouping-Based Hybrid Light Transport Model For Enhancing The Efficiency And Accuracy In Bioluminescence Tomography

MEDICAL IMAGING 2020: PHYSICS OF MEDICAL IMAGING(2021)

引用 2|浏览11
暂无评分
摘要
Bioluminescence tomography (BLT) is a promising molecular imaging tool in monitoring non-invasively physiological and pathological processes in vivo at the cellular and molecular levels. And the radiative transfer equation (RTE) has successfully been used as a standard model for describing the propagation of visible and near infrared photons trough biological tissues. However in practical application, implementation of RTE is extremely complicated for complex biological tissue. And several approximations of the RTE were applied to model the light transport in a turbid medium, such as the diffusion equation (DE) and the simplified spherical harmonic approximation equation (SPN). However, DE provides a high computational efficiency and is only valid in the high scattering region, while SPN has a large demand for memory space, which makes it difficult for SPN to be used on the fine mesh and limits its application in practice. In this paper, we provided a new finite element mesh regrouping-based hybrid light transport model in BLT. Based on the optical property of biological tissue, the finite element mesh were grouped into high-scattering and low-scattering regions. And based on the theory of light transport, hybrid third-order simplified spherical harmonic approximate-diffusion equation model (HSDM) was used to forward light transport model. In numerical simulation experiments, accuracy and efficiency of our proposed method were evaluated. Results showed that the hybrid light transport model achieved a better balance between accuracy and efficiency compared with the DE and the SP3 models. And it was best suited as a light transport model for Bioluminescence Tomography.
更多
查看译文
关键词
Hybrid light transport model, Bioluminescence Tomography(BLT), Tissue optics
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要