Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013 (2013), Article ID 690140, 13 pages
http://dx.doi.org/10.1155/2013/690140
Research Article

Application of Self-Organizing Artificial Neural Networks on Simulated Diffusion Tensor Images

Institute of Biomedical Engineering, Bogazici University, Kandilli Campus, 34684 Istanbul, Turkey

Received 4 February 2013; Accepted 18 March 2013

Academic Editor: Matjaz Perc

Copyright © 2013 Dilek Göksel-Duru and Mehmed Özkan. 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.

Linked References

  1. P. J. Basser, S. Pajevic, C. Pierpaoli, J. Duda, and A. Aldroubi, “In vivo fiber tractography using DT-MRI data,” Magnetic Resonance in Medicine, vol. 44, pp. 625–632, 2000. View at Publisher · View at Google Scholar
  2. D. Le Bihan, J. F. Mangin, C. Poupon et al., “Diffusion tensor imaging: concepts and applications,” Journal of Magnetic Resonance Imaging, vol. 13, no. 4, pp. 534–546, 2001. View at Publisher · View at Google Scholar · View at Scopus
  3. H. Johansen-Berg and T. E. J. Behrens, “Just pretty pictures? What diffusion tractography can add in clinical neuroscience,” Current Opinion in Neurology, vol. 19, no. 4, pp. 379–385, 2006. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, Upper Saddle River, NJ, USA, 2005.
  5. T. Kohonen, Self-Organizing Maps, Springer, Berlin, Germany, 2001.
  6. X. Wang, F. Kang, J. Li, and X. Wang, “Inverse parametric analysis of seismic permanent deformation for earth-rockfill dams using artificial neural networks,” Mathematical Problems in Engineering, vol. 2012, Article ID 383749, 19 pages, 2012. View at Publisher · View at Google Scholar
  7. C. Yao, X. Gao, and Y. Yu, “Wind speed forecasting by wavelet neural networks: a comparative study,” Mathematical Problems in Engineering, vol. 2013, Article ID 395815, 7 pages, 2013. View at Publisher · View at Google Scholar
  8. C. Ding, W. Wang, X. Wang, and M. Baumann, “A neural network model for driver’s lane-changing trajectory prediction in Urban traffic flow,” Mathematical Problems in Engineering, vol. 2013, Article ID 967358, 8 pages, 2013. View at Publisher · View at Google Scholar
  9. I. Lou and Y. Zhao, “Sludge bulking prediction using principle component regression and artificial neural network,” Mathematical Problems in Engineering, vol. 2012, Article ID 237693, 17 pages, 2012. View at Publisher · View at Google Scholar
  10. F. Sedaghati, A. Nahavandi, M. A. Badamchizadeh, S. Ghaemi, and M. A. Fallah, “PV maximum power-point tracking by using artificial neural network,” Mathematical Problems in Engineering, vol. 2012, Article ID 506709, 10 pages, 2012. View at Publisher · View at Google Scholar
  11. P. Cheng, V. A. Magnotta, D. Wu et al., “Evaluation of the GTRACT diffusion tensor tractography algorithm: a validation and reliability study,” NeuroImage, vol. 31, no. 3, pp. 1075–1085, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. R. Watts, C. Liston, S. Niogi, and A. M. Uluǧ, “Fiber tracking using magnetic resonance diffusion tensor imaging and its applications to human brain development,” Mental Retardation and Developmental Disabilities Research Reviews, vol. 9, no. 3, pp. 168–177, 2003. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Mori, B. J. Crain, V. P. Chacko, and P. C. M. van Zijl, “Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging,” Annals of Neurology, vol. 45, pp. 265–269, 1999. View at Publisher · View at Google Scholar
  14. D. K. Jones, M. A. Horsfield, and A. Simmons, “Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging,” Magnetic Resonance in Medicine, vol. 42, pp. 515–525, 1999. View at Publisher · View at Google Scholar
  15. D. K. Jones, A. Simmons, S. C. R. Williams, and M. A. Horsfield, “Non-invasive assessment of axonal fiber connectivity in the human brain via diffusion tensor MRI,” Magnetic Resonance in Medicine, vol. 42, pp. 37–41, 1999. View at Publisher · View at Google Scholar
  16. S. C. Deoni and D. K. Jones, “Generation of a common diffusion tensor imaging dataset,” in Proceedings of the ISMRM Workshop on Methods for Quantitative Diffusion Methods for Quantitative Diffusion MRI of Human Brain, 2005, http://cubric.psych.cf.ac.uk/commondti.
  17. M. Spratling and G. M. Hayes, “A self-organising neural network for modelling cortical development,” in Proceedings of European Symposium on Artificial Neural Networks (ESANN '98), pp. 333–338, 1998.
  18. N. F. Lori, E. Akbudak, J. S. Shimony et al., “Diffusion tensor fiber tracking of human brain connectivity: aquisition methods, reliability analysis and biological results,” NMR in Biomedicine, vol. 15, no. 7-8, pp. 494–515, 2002. View at Google Scholar · View at Scopus
  19. M. Lazar and A. L. Alexander, “White matter tractography using diffusion tensor deflection,” Human Brain Mapping, vol. 18, no. 4, pp. 306–321, 2003. View at Publisher · View at Google Scholar
  20. Q. Wang, M. Perc, Z. Duan, and G. Chen, “Synchronization transitions on scale-free neuronal networks due to finite information transmission delays,” Physical Review E, vol. 80, no. 2, Article ID 026206, 2009. View at Publisher · View at Google Scholar · View at Scopus
  21. Q. Wang, G. Chen, and M. Perc, “Synchronous bursts on scale-free neuronal networks with attractive and repulsive coupling,” PLoS ONE, vol. 6, no. 1, Article ID e15851, 2011. View at Publisher · View at Google Scholar · View at Scopus
  22. Q. Wang and G. Chen, “Delay-induced intermittent transition of synchronization in neuronal networks with hybrid synapses,” Chaos, vol. 21, no. 1, Article ID 013123, 2011. View at Publisher · View at Google Scholar · View at Scopus
  23. Q. Wang, H. Zhang, and G. Chen, “Effect of the heterogeneous neuron and information transmission delay on stochastic resonance of neuronal networks,” Chaos, vol. 22, Article ID 043123, 7 pages, 2012. View at Publisher · View at Google Scholar
  24. Q. Wang, M. Perc, Z. Duan, and G. Chen, “Delay-enhanced coherence of spiral waves in noisy Hodgkin-Huxley neuronal networks,” Physics Letters A, vol. 372, pp. 5681–5687, 2008. View at Publisher · View at Google Scholar
  25. Q. Wang and Y. Zheng, “Effects of information transmission delay and channel blocking on synchronization in scale-free Hodgkin-Huxley neuronal networks,” Acta Mechanica Sinica, vol. 27, no. 6, pp. 1052–1058, 2011. View at Publisher · View at Google Scholar
  26. P. J. Basser, J. Mattiello, and D. LeBihan, “MR diffusion tensor spectroscopy and imaging,” Biophysical Journal, vol. 66, no. 1, pp. 259–267, 1994. View at Google Scholar · View at Scopus
  27. D. LeBihan, C. Poupon, A. Amadon, and F. Lethimonnier, “Artifacts and pitfalls in diffusion MRI,” Journal of Magnetic Resonance Imaging, vol. 24, pp. 478–488, 2006. View at Publisher · View at Google Scholar
  28. C. Qin, N. Kang, and N. Cao, “Performance evaluation of anisotropic diffusion simulation based tractography on phantom images,” in Proceedings of the 45th Annual ACM Southeast Conference (ACMSE '07), pp. 521–522, New York, NY, USA, July 2007. View at Publisher · View at Google Scholar · View at Scopus
  29. S. C. Mang, D. Logashenko, D. Gembris, G. Wittum, W. Grodd, and U. Klose, “Diffusion simulation-based fiber tracking using time-of-arrival maps: a comparison with standard methods,” Magnetic Resonance Materials in Physics, Biology and Medicine, vol. 23, no. 5-6, pp. 391–398, 2010. View at Publisher · View at Google Scholar · View at Scopus