Open AccessAutomatic selection of regularization parameters for dynamic fluorescence molecular tomography: a comparison of L-curve and U-curve methods
Author(s) -
Maomao Chen,
Han Su,
Yuan Zhou,
Chuangjian Cai,
Dong Zhang,
Jianwen Luo
Publication year - 2016
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.7.005021
Subject(s) - imaging phantom , parametric statistics , computer science , image quality , regularization (linguistics) , robustness (evolution) , artificial intelligence , tomography , iterative reconstruction , computer vision , algorithm , mathematics , optics , physics , image (mathematics) , chemistry , statistics , biochemistry , gene
Dynamic fluorescence molecular tomography (FMT) is a promising technique for the study of the metabolic process of fluorescent agents in the biological body in vivo , and the quality of the parametric images relies heavily on the accuracy of the reconstructed FMT images. In typical dynamic FMT implementations, the imaged object is continuously monitored for more than 50 minutes. During each minute, a set of the fluorescent measurements is acquired and the corresponding FMT image is reconstructed. It is difficult to manually set the regularization parameter in the reconstruction of each FMT image. In this paper, the parametric images obtained with the L-curve and U-curve methods are quantitatively evaluated through numerical simulations, phantom experiments and in vivo experiments. The results illustrate that the U-curve method obtains better accuracy, stronger robustness and higher noise-resistance in parametric imaging. Therefore, it is a promising approach to automatic selection of the regularization parameters for dynamic FMT.