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Proceedings Paper

Tomographic fluorescence reconstruction by a spectral projected gradient pursuit method
Author(s): Jinzuo Ye; Yu An; Yamin Mao; Shixin Jiang; Xin Yang; Chongwei Chi; Jie Tian
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Paper Abstract

In vivo fluorescence molecular imaging (FMI) has played an increasingly important role in biomedical research of preclinical area. Fluorescence molecular tomography (FMT) further upgrades the two-dimensional FMI optical information to three-dimensional fluorescent source distribution, which can greatly facilitate applications in related studies. However, FMT presents a challenging inverse problem which is quite ill-posed and ill-conditioned. Continuous efforts to develop more practical and efficient methods for FMT reconstruction are still needed. In this paper, a method based on spectral projected gradient pursuit (SPGP) has been proposed for FMT reconstruction. The proposed method was based on the directional pursuit framework. A mathematical strategy named the nonmonotone line search was associated with the SPGP method, which guaranteed the global convergence. In addition, the Barzilai-Borwein step length was utilized to build the new step length of the SPGP method, which was able to speed up the convergence of this gradient method. To evaluate the performance of the proposed method, several heterogeneous simulation experiments including multisource cases as well as comparative analyses have been conducted. The results demonstrated that, the proposed method was able to achieve satisfactory source localizations with a bias less than 1 mm; the computational efficiency of the method was one order of magnitude faster than the contrast method; and the fluorescence reconstructed by the proposed method had a higher contrast to the background than the contrast method. All the results demonstrated the potential for practical FMT applications with the proposed method.

Paper Details

Date Published: 5 March 2015
PDF: 7 pages
Proc. SPIE 9316, Multimodal Biomedical Imaging X, 931604 (5 March 2015); doi: 10.1117/12.2077165
Show Author Affiliations
Jinzuo Ye, Institute of Automation (China)
Yu An, Beijing Jiaotong Univ. (China)
Yamin Mao, Institute of Automation (China)
Shixin Jiang, Beijing Jiaotong Univ. (China)
Xin Yang, Institute of Automation (China)
Chongwei Chi, Institute of Automation (China)
Jie Tian, Institute of Automation (China)


Published in SPIE Proceedings Vol. 9316:
Multimodal Biomedical Imaging X
Fred S. Azar; Xavier Intes, Editor(s)

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