Open AccessImproved sparse reconstruction for fluorescence molecular tomography with L_1/2 regularization
Author(s) -
Hongbo Guo,
Jingjing Yu,
Xiaowei He,
Yi Hou,
Feifei Dong,
Shuling Zhang
Publication year - 2015
Publication title -
biomedical optics express
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.362
H-Index - 86
ISSN - 2156-7085
DOI - 10.1364/boe.6.001648
Subject(s) - computer science , regularization (linguistics) , iterative reconstruction , robustness (evolution) , algorithm , norm (philosophy) , tomography , compressed sensing , visualization , elastic net regularization , image resolution , artificial intelligence , optics , chemistry , physics , biochemistry , feature selection , political science , law , gene
Fluorescence molecular tomography (FMT) is a promising imaging technique that allows in vivo visualization of molecular-level events associated with disease progression and treatment response. Accurate and efficient 3D reconstruction algorithms will facilitate the wide-use of FMT in preclinical research. Here, we utilize L1/2-norm regularization for improving FMT reconstruction. To efficiently solve the nonconvex L1/2-norm penalized problem, we transform it into a weighted L1-norm minimization problem and employ a homotopy-based iterative reweighting algorithm to recover small fluorescent targets. Both simulations on heterogeneous mouse model and in vivo experiments demonstrated that the proposed L1/2-norm method outperformed the comparative L1-norm reconstruction methods in terms of location accuracy, spatial resolution and quantitation of fluorescent yield. Furthermore, simulation analysis showed the robustness of the proposed method, under different levels of measurement noise and number of excitation sources.