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BioMed Research International
Volume 2016, Article ID 3094698, 9 pages
http://dx.doi.org/10.1155/2016/3094698
Research Article

An Improved Total Variation Minimization Method Using Prior Images and Split-Bregman Method in CT Reconstruction

Key Laboratory of Optoelectronics Technology & System, Chongqing University, Ministry of Education, Chongqing 400044, China

Received 20 April 2016; Accepted 14 July 2016

Academic Editor: Shouping Zhu

Copyright © 2016 Luzhen Deng 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.

Linked References

  1. D. J. Brenner and E. J. Hall, “Computed tomography—an increasing source of radiation exposure,” The New England Journal of Medicine, vol. 357, no. 22, pp. 2277–2284, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Zhang, W. Li, and G. Tang, “Study on image reconstruction algorithm of filtered backprojection,” Journal of Xianyang Normal University, vol. 23, pp. 47–49, 2008. View at Google Scholar
  3. R. Gordon, R. Bender, and G. T. Herman, “Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography,” Journal of Theoretical Biology, vol. 29, no. 3, pp. 471–481, 1970. View at Publisher · View at Google Scholar · View at Scopus
  4. A. H. Andersen and A. C. Kak, “Simultaneous Algebraic Reconstruction Technique (SART): a superior implementation of the ART algorithm,” Ultrasonic Imaging, vol. 6, no. 1, pp. 81–94, 1984. View at Publisher · View at Google Scholar · View at Scopus
  5. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. E. Y. Sidky and X. C. Pan, “Image reconstruction in circular cone-beam computed tomography by constrained, total-variation minimization,” Physics in Medicine and Biology, vol. 53, no. 17, pp. 4777–4807, 2008. View at Publisher · View at Google Scholar · View at Scopus
  7. M. Chen, D. Mi, P. He, L. Deng, and B. Wei, “A CT reconstruction algorithm based on L1/2 regularization,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 862910, 8 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  8. L.-Z. Deng, P. Feng, M.-Y. Chen, P. He, Q.-S. Vo, and B. Wei, “A CT reconstruction algorithm based on non-aliasing contourlet transform and compressive sensing,” Computational and Mathematical Methods in Medicine, vol. 2014, Article ID 753615, 9 pages, 2014. View at Publisher · View at Google Scholar · View at Scopus
  9. L. Z. Deng, D. L. Mi, P. He et al., “A CT reconstruction approach from sparse projection with adaptive-weighted diagonal total-variation in biomedical application,” Bio-Medical Materials and Engineering, vol. 26, no. 1, pp. S1685–S1693, 2015. View at Publisher · View at Google Scholar
  10. M. Y. Chen, Y. Ren, P. Feng et al., “Computed tomography image reconstruction from few-views data by multi-directional total variation,” Journal of Medical Imaging and Health Informatics, vol. 5, no. 2, pp. 309–316, 2015. View at Google Scholar
  11. G.-H. Chen, P. Thériault-Lauzier, J. Tang et al., “Time-resolved interventional cardiac C-arm cone-beam CT: an application of the piccs algorithm,” IEEE Transactions on Medical Imaging, vol. 31, no. 4, pp. 907–923, 2012. View at Publisher · View at Google Scholar · View at Scopus
  12. Q. Xu, H. Y. Yu, X. Q. Mou, L. Zhang, J. Hsieh, and G. Wang, “Low-dose X-ray CT reconstruction via dictionary learning,” IEEE Transactions on Medical Imaging, vol. 31, no. 9, pp. 1682–1697, 2012. View at Publisher · View at Google Scholar · View at Scopus
  13. H. Yu, S. Zhao, E. A. Hoffman, and G. Wang, “Ultra-low dose lung CT perfusion regularized by a previous scan,” Academic Radiology, vol. 16, no. 3, pp. 363–373, 2009. View at Publisher · View at Google Scholar · View at Scopus
  14. J. Ma, H. Zhang, Y. Gao et al., “Iterative image reconstruction for cerebral perfusion CT using a pre-contrast scan induced edge-preserving prior,” Physics in Medicine and Biology, vol. 57, no. 22, pp. 7519–7542, 2012. View at Publisher · View at Google Scholar · View at Scopus
  15. L. Ouyang, T. Solberg, and J. Wang, “Noise reduction in low-dose cone beam CT by incorporating prior volumetric image information,” Medical Physics, vol. 39, no. 5, pp. 2569–2577, 2012. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Goldstein and S. Osher, “The split Bregman method for L1-regularized problems,” SIAM Journal on Imaging Sciences, vol. 2, no. 2, pp. 323–343, 2009. View at Publisher · View at Google Scholar · View at MathSciNet
  17. S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, Cambridge, UK, 2004. View at Publisher · View at Google Scholar · View at MathSciNet
  18. M. Y. Chen, Y. Xi, W. X. Cong, B. D. Liu, B. Wei, and G. Wang, “X-ray CT geometrical calibration via locally linear embedding,” Journal of X-Ray Science and Technology, vol. 24, no. 2, pp. 241–256, 2016. View at Publisher · View at Google Scholar
  19. Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, “Image quality assessment: from error visibility to structural similarity,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600–612, 2004. View at Publisher · View at Google Scholar · View at Scopus