A Monte-Carlo-Based Network Method for Source Positioning in Bioluminescence Tomography

Abstract

We present an approach based on the improved Levenberg Marquardt (LM) algorithm of backpropagation (BP) neural network to estimate the light source position in bioluminescent imaging. For solving the forward problem, the table-based random sampling algorithm (TBRS), a fast Monte Carlo simulation method we developed before, is employed here. Result shows that BP is an effective method to position the light source.

References

1. G. Wang, W. Cong, Y. Li et al., “Recent development in bioluminescence tomography,” in Proceedings of the 3rd IEEE International Symposium on Biomedical Imaging: From Nano to Macro, (ISBI '06), pp. 678–681, Arlington, Va, USA, 2006. View at: Google Scholar
2. G. Wang, W. Cong, D. Kumar et al., “In vivo mouse studies with bioluminescence tomography,” Optics Express, vol. 14, no. 17, pp. 7801–7809, 2006. View at: Publisher Site | Google Scholar
3. W. Cong, G. Wang, D. Kumar et al., “Practical reconstruction method for bioluminescence tomography,” Optics Express, vol. 13, no. 18, pp. 6756–6771, 2005. View at: Publisher Site | Google Scholar
4. X. Gu, Q. Zhang, L. Larcom, and H. Jiang, “Three-dimensional bioluminescence tomography with model-based reconstruction,” Optics Express, vol. 12, no. 17, pp. 3996–4000, 2004. View at: Publisher Site | Google Scholar
5. Y. Lv, J. Tian, W. Cong et al., “A multilevel adaptive finite element algorithm for bioluminescence tomography,” Optics Express, vol. 14, no. 18, pp. 8211–8223, 2006. View at: Publisher Site | Google Scholar
6. I. V. Yaroslavsky, H. J. Schwarzmaier, A. N. Yaroslavsky, and V. V. Yuchin, “Radiative transfer equation and its diffusion approximation in the frequency domain technique: a comparison,” in Photon Transport in Highly Scattering Tissue, vol. 2326 of Proceedings of SPIE, pp. 465–474, Lille, France, September 1995. View at: Publisher Site | Google Scholar
7. H. Li, J. Tian, F. Zhu et al., “A mouse optical simulation environment (MOSE) to investigate bioluminescent phenomena in the living mouse with the Monte Carlo method,” Academic Radiology, vol. 11, no. 9, pp. 1029–1038, 2004. View at: Publisher Site | Google Scholar
8. X. Zhang and J. Bai, “A table-based random sampling simulation for bioluminescence tomography,” International Journal of Biomedical Imaging, vol. 2006, Article ID 83820, 8 pages, 2006. View at: Publisher Site | Google Scholar
9. T. O. Duda and P. E. Hart, “Multilayer neural networks,” in Pattern Classification, p. 287, Wiley-Interscience, New York, NY, USA, 2nd edition, 2000, chapter 6. View at: Google Scholar
10. L.-Q. Li, S.-L. Wang, and B.-T. Zhao, “Improved Levenberg-Marquardt algorithm for BP neural network and its application in predicting the particle impact damping,” Journal of University of Shanghai for Science and Technology, vol. 28, no. 4, pp. 331–333, 2006. View at: Google Scholar

Copyright

Copyright © 2007 Zhun Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.