Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a high...Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a highly ill-posed problem to fulfll 3D reconstruction inFMT.In this contribution,we propose an efficient iterative algorithm to solve the large-scalereconstruction problem,in which the sparsity of fluorescent targets is taken as useful a prioriinformation in designing the reconstruction algorithm.In the implementation,a fast sparseapproximation scheme combined with a stage-wise learning strategy enable the algorithm to dealwith the ill-posed inverse problem at reduced computational costs.We validate the proposed fastiterative method with numerical simulation on a digital mouse model.Experimental results demonstrate that our method is robust for different finite element meshes and different Poissonnoise levels.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.61372046)the Research Fund for the Doctoral Program ofHigher Education of China(New Teachers)(Grant No.20116101120018)+4 种基金the China Postdoctoral Sci-ence_Foundation_Funded Project(Grant_Nos.2011M501467 and 2012T50814)the Natural Sci-ence Basic Research Plan in Shaanxi Province of China(Grant No.2011JQ1006)the Fund amental Research Funds for the Central Universities(Grant No.GK201302007)Science and Technology Plan Program in Shaanxi Province of China(Grant Nos.2012 KJXX-29 and 2013K12-20-12)the Scienceand Technology Plan Program in Xi'an of China(Grant No.CXY 1348(2)).
文摘Fluorescence molecular tomography(FMT)is a fast-developing optical imaging modalitythat has great potential in early diagnosis of disease and drugs development.However,recon-struction algorithms have to address a highly ill-posed problem to fulfll 3D reconstruction inFMT.In this contribution,we propose an efficient iterative algorithm to solve the large-scalereconstruction problem,in which the sparsity of fluorescent targets is taken as useful a prioriinformation in designing the reconstruction algorithm.In the implementation,a fast sparseapproximation scheme combined with a stage-wise learning strategy enable the algorithm to dealwith the ill-posed inverse problem at reduced computational costs.We validate the proposed fastiterative method with numerical simulation on a digital mouse model.Experimental results demonstrate that our method is robust for different finite element meshes and different Poissonnoise levels.
