摘要
Fluorescence molecular tomography(FMT) aims at tomographicallyresolving the fluorescent targets deeply inside small animal based on transmission boundary measurements. The image reconstruction of FMT isknown to be highlyill-posed, due to the highly scatteringnatureofbiologicaltissue.Hence,priorinformationisusuallyrequired for successful reconstruction. In this paper, a novel reconstruction method incorporating shape priors is proposed for 2D FMT. The fluorescent targets were assumed of round shape, which was practically appropriate for approximating various shapes inside diffusive medium. Compared to the traditional pixel based reconstruction, the number of unknowns was greatly reduced to a few control parameters of round shapes. A hybrid genetic algorithm was proposed to recover the shape parameters. The numerical experiments showed that the proposed method significantly improves the imaging accuracy, offering clearer target boundaries and better resolution. Comparison results also demonstrated that the hybridization of genetic algorithm and Newton-typesearchwaspivotalandimportantforrobustlyfindingthegloballyoptimalshape parameters.
Fluorescence molecular tomography (FMT) aims at tomographically re-solving the fluorescent targets deeply inside small animal based on transmission boundary measurements. The image reconstruction of FMT is known to be highly ill-posed, due to the highly scattering nature of biological tissue. Hence, prior information is usually required for successful reconstruction. In this paper, a novel reconstruction method incorporating shape priors is proposed for 2D FMT. The fluorescent targets were assumed of round shape, which was practically appropriate for approximating various shapes inside diffusive medium. Compared to the traditional pixel based reconstruction, the number of unknowns was greatly reduced to a few control parameters of round shapes. A hybrid genetic algorithm was proposed to recover the shape parameters. The numerical experiments showed that the proposed method significantly improves the imaging accuracy, offering clearer target boundaries and better resolution. Comparison results also demonstrated that the hybridization of genetic algorithm and Newton-type search was pivotal and important for robustly finding the globally optimal shape parameters.
基金
State Key Laboratory of Software Development Environment
grant number:SKLSDE-2011ZX-12
the National Natural Science Foundation of China
grant number:61108084,61101008
Research Fund for the Doctoral Program of Higher Education of China
grant number:20111102120039
Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education,the Fundamental Research Funds for the Central Universities
