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Proceedings Paper

Bioluminescence tomography based on Bayesian approach
Author(s): Jinchao Feng; Kebin Jia; Jie Tian; Guorui Yan; Chenghu Qin
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Paper Abstract

As a new mode of molecular imaging, bioluminescence tomography (BLT) will have significant effect on revealing the molecular and cellular information in vivo at the whole-body small animal level because of its high sensitive detection and facile operation. However, BLT is an ill-posed problem, it is necessary to incorporate a priori knowledge into the tomographic algorithm. In this paper, a novel Bayesian reconstruction algorithm for BLT is firstly proposed. In the algorithm, a priori permissible source region strategy is incorporated into the Bayesian network to reduce the ill-posedness of BLT. Then a generalized adaptive Gaussian Markov random field (GAGMRF) prior model for unknown source density estimation is developed to further reduce the ill-posedness of BLT on the basis of adaptive finite element analysis. Finally, the algorithm maximizes the log posterior probability with respect to a noise parameter and the unknown source density, the distribution of bioluminescent source can be reconstructed. In addition, the novel tomography algorithm based adaptive finite element makes the method more appropriate for complex phantom such as real mouse. In the numerical simulation, a heterogeneous phantom is used to evaluate the performance of the proposed algorithm with the Monte Carlo based synthetic data. The accurate localization of bioluminescent source and quantitative results show the effectiveness and potential of the tomographic algorithm for BLT.

Paper Details

Date Published: 27 February 2009
PDF: 8 pages
Proc. SPIE 7262, Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging, 72620R (27 February 2009); doi: 10.1117/12.811330
Show Author Affiliations
Jinchao Feng, Beijing Univ. of Technology (China)
Kebin Jia, Beijing Univ. of Technology (China)
Jie Tian, Institute of Automation (China)
Xidian Univ. (China)
Guorui Yan, Institute of Automation (China)
Chenghu Qin, Institute of Automation (China)

Published in SPIE Proceedings Vol. 7262:
Medical Imaging 2009: Biomedical Applications in Molecular, Structural, and Functional Imaging
Xiaoping P. Hu; Anne V. Clough, Editor(s)

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