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Computational and Mathematical Methods in Medicine
Volume 2015 (2015), Article ID 831790, 12 pages
http://dx.doi.org/10.1155/2015/831790
Research Article

A Model of Regularization Parameter Determination in Low-Dose X-Ray CT Reconstruction Based on Dictionary Learning

1Medical Imaging Laboratory, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou 215163, China
2Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
3University of Chinese Academy of Sciences, Beijing 100049, China
4PET Center, Huashan Hospital, Fudan University, Shanghai 200235, China

Received 13 March 2015; Accepted 11 June 2015

Academic Editor: Lin Lu

Copyright © 2015 Cheng Zhang 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. X. Han, J. Bian, E. L. Ritman, E. Y. Sidky, and X. Pan, “Optimization-based reconstruction of sparse images from few-view projections,” Physics in Medicine and Biology, vol. 57, no. 16, pp. 5245–5273, 2012. View at Publisher · View at Google Scholar · View at Scopus
  2. B. Song, J. Park, and W. Song, “SU-E-J-14: a novel, fast, variable step size gradient method for solving simultaneous algebraic reconstruction technique (SART)-type reconstructions: an example application to CBCT,” Medical Physics, vol. 38, no. 6, article 3444, 2011. View at Publisher · View at Google Scholar
  3. E. J. Candes, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  4. E. J. Candes and T. Tao, “Near-optimal signal recovery from random projections: universal encoding strategies?” IEEE Transactions on Information Theory, vol. 52, no. 12, pp. 5406–5425, 2006. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  5. J. Wang and B. Shim, “On the recovery limit of sparse signals using orthogonal matching pursuit,” IEEE Transactions on Signal Processing, vol. 60, no. 9, pp. 4973–4976, 2012. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. P. T. Lauzier, J. Tang, and G.-H. Chen, “Prior image constrained compressed sensing: implementation and performance evaluation,” Medical Physics, vol. 39, no. 1, pp. 66–80, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. W. W. Hager, D. T. Phan, and H. Zhang, “Gradient-based methods for sparse recovery,” SIAM Journal on Imaging Sciences, vol. 4, no. 1, pp. 146–165, 2011. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  8. R. Volz and S. Close, “Inverse filtering of radar signals using compressed sensing with application to meteors,” Radio Science, vol. 47, no. 4, Article ID RS0N05, 2012. View at Publisher · View at Google Scholar · View at Scopus
  9. G. Wang, Y. Bresler, and V. Ntziachristos, “Guest editorial compressive sensing for biomedical imaging,” IEEE Transactions on Medical Imaging, vol. 30, no. 5, pp. 1013–1016, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. 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
  11. J. C. Park, B. Song, J. S. Kim et al., “Fast compressed sensing-based CBCT reconstruction using Barzilai-Borwein formulation for application to on-line IGRT,” Medical Physics, vol. 39, no. 3, pp. 1207–1217, 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. M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311–4322, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. J. A. Tropp and A. C. Gilbert, “Signal recovery from random measurements via orthogonal matching pursuit,” IEEE Transactions on Information Theory, vol. 53, no. 12, pp. 4655–4666, 2007. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. I. A. Elbakri and J. A. Fessler, “Statistical image reconstruction for polyenergetic X-ray computed tomography,” IEEE Transactions on Medical Imaging, vol. 21, no. 2, pp. 89–99, 2002. View at Publisher · View at Google Scholar · View at Scopus
  16. K. Karl and Z. Jun, “Iterative choices of regularization parameters in linear inverse problems,” Inverse Problems, vol. 14, no. 5, pp. 1247–1264, 1998. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. J. Feng, C. Qin, K. Jia et al., “An adaptive regularization parameter choice strategy for multispectral bioluminescence tomography,” Medical Physics, vol. 38, no. 11, pp. 5933–5944, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. C. Clason, B. T. Jin, and K. Kunisch, “A semismooth Newton method for L1 data fitting with automatic choice of regularization parameters and noise calibration,” SIAM Journal on Imaging Sciences, vol. 3, no. 2, pp. 199–231, 2010. View at Publisher · View at Google Scholar
  19. C. Kamphuis and F. J. Beekman, “Accelerated iterative transmission CT reconstruction using an ordered subsets convex algorithm,” IEEE Transactions on Medical Imaging, vol. 17, no. 6, pp. 1101–1105, 1998. View at Publisher · View at Google Scholar · View at Scopus