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Applied Magnetic Resonance

, Volume 50, Issue 1–3, pp 103–120 | Cite as

An Improved Calibration Framework for Iterative Self-Consistent Parallel Imaging Reconstruction (SPIRiT)

  • Zhenzhou Wu
  • Jianbing Zhu
  • Yan Chang
  • Yajie Xu
  • Xiaodong YangEmail author
Original Paper
  • 58 Downloads

Abstract

The image quality of iterative self-consistent parallel imaging reconstruction (SPIRiT) algorithm highly depends on the accuracy of linear coefficients which can be easily influenced by k-space noise. In this study, an improved calibration framework for SPIRiT is presented to reduce noise-induced errors and to adaptively generate optimal linear weighting coefficients. Specifically, the auto-calibration signals (ACS) are first mapped to a high-dimensional feature space through a polynomial mapping, and the optimal coefficients are adaptively obtained in this new feature space with discrepancy-based Tikhonov regularization and then truncated for SPIRiT reconstruction. Phantom and in vivo brain reconstruction were, respectively, performed and this calibration framework was mainly evaluated in Cartesian k-space-based SPIRiT reconstruction. In both phantom and in vivo reconstructions, noise-induced errors can be reduced by polynomial mapping and optimal regularization parameter, which improves the accuracy of linear coefficients. Both qualitative and quantitative results demonstrated that the proposed calibration framework resulted in better image quality without loss of resolution compared with the conventional calibration at different acceleration factors. The proposed calibration framework can effectively improve SPIRiT image quality.

Notes

Acknowledgements

This work was supported by the Instrument Developing Project of the Chinese Academy of Sciences (Grant No. YZ201445) and National Natural Science Foundation of China (Grant No. 11505281; 11675254).

References

  1. 1.
    D.K. Sodickson, W.J. Manning, Magn. Reson. Med. 38, 591–603 (1997)CrossRefGoogle Scholar
  2. 2.
    K.P. Pruessmann, M. Weiger, M.B. Scheidegger, P. Boesiger, Magn. Reson. Med. 42, 952–962 (1999)CrossRefGoogle Scholar
  3. 3.
    K. Pruessmann, M. Weiger, P. Bornert, P. Boesiger, Magn. Reson. Med. 46, 638–651 (2001)CrossRefGoogle Scholar
  4. 4.
    L. Chen, Y. Chang, L. Wang, X. Yang, Comput. Meas. Control. 23, 4177–4179 (2015)Google Scholar
  5. 5.
    A.A. Samsonov, E.G. Kholmovski, D.L. Parker, C.R. Johnson, Magn. Reson. Med. 52, 1397–1406 (2004)CrossRefGoogle Scholar
  6. 6.
    M.A. Griswold, P.M. Jakob, R.M. Heidemann, M. Nittka, V. Jellus, J. Wang, B. Kiefer, A. Haase, Magn. Reson. Med. 47, 1202–1210 (2002)CrossRefGoogle Scholar
  7. 7.
    Z. Wang, J. Wang, J.A. Detre, Magn. Reson. Med. 54, 738–742 (2005)CrossRefGoogle Scholar
  8. 8.
    R. Nana, X. Hu, Magn. Reson. Imaging 28, 119–128 (2010)CrossRefGoogle Scholar
  9. 9.
    F.H. Lin, K.K. Kwong, J.W. Belliveau, L.L. Wald, Magn. Reson. Med. 51, 559–567 (2004)CrossRefGoogle Scholar
  10. 10.
    W. Liu, X. Tang, Y. Ma, J. Gao, Magn. Reson. Med. 69, 1109–1114 (2013)CrossRefGoogle Scholar
  11. 11.
    H. Wang, D. Liang, K.F. King, G. Nagarsekar, Y. Chang, L. Ying, Magn. Reson. Med. 67, 1042–1053 (2012)CrossRefGoogle Scholar
  12. 12.
    B. Sharif, Y. Bresler, in: Proceedings of the IEEE International Symposium on Biomedical Imaging, Chicago, 2011, p. 52–56Google Scholar
  13. 13.
    Y. Chang, D. Liang, L. Ying, Magn. Reson. Med. 68, 730–740 (2012)CrossRefGoogle Scholar
  14. 14.
    D. Wang, S. Bao, Chin. J. Med. Imaging Technol. 27, 1688–1693 (2011)Google Scholar
  15. 15.
    L. Chen, Y. Chang, L. Wang, L. Wang, Y. Xu, G. Zhang, X. Yang, Chin. J. Med. Phys. 32, 617–621 (2015)Google Scholar
  16. 16.
    M. Lustig, J.M. Pauly, Magn. Reson. Med. 64, 457–471 (2010)Google Scholar
  17. 17.
    P. Qu, C. Wang, G.X. Shen, J. Magn. Reson. Imaging 24, 248–255 (2006)CrossRefGoogle Scholar
  18. 18.
    X. Shi, X. Ma, W. Wu, F. Huang, C. Yuan, H. Guo, Magn. Reson. Med. 73, 1775–1785 (2015)CrossRefGoogle Scholar
  19. 19.
    M. Murphy, M. Alley, J. Demmel, K. Keutzer, S. Vasanawala, M. Lustig, IEEE Trans. Med. Imaging. 31, 1250–1262 (2012)CrossRefGoogle Scholar
  20. 20.
    K.H. Jin, D. Lee, J.C. Ye, IEEE Trans. Comput. Imaging. 2, 480–495 (2016)MathSciNetCrossRefGoogle Scholar
  21. 21.
    C. Liao, Y. Chen, X. Cao, S. Chen, H. He, M. Mani, M. Jacob, V. Magnotta, J. Zhong, Magn. Reson. Med. 77, 1359–1366 (2017)CrossRefGoogle Scholar
  22. 22.
    M. Lustig, D. Donoho, J.M. Pauly, Magn. Reson. Med. 58, 1182–1195 (2007)CrossRefGoogle Scholar
  23. 23.
    J. Zhang, J. Shi, H. Guang, S. Zuo, F. Liu, J. Bai, J. Luo, IEEE Trans. Biomed. Eng. 63, 1107–1115 (2016)CrossRefGoogle Scholar
  24. 24.
    D.J. Holland, D.M. Malioutov, A. Blake, A.J. Sederman, L.F. Gladden, J. Magn. Reson. 203, 236–246 (2010)ADSCrossRefGoogle Scholar
  25. 25.
    Y. Chen, X. Ye, F. Huang, Inverse Problems Imaging 4, 223–240 (2017)CrossRefGoogle Scholar
  26. 26.
    R. Otazo, E. Candès, D.K. Sodickson, Magn. Reson. Med. 73, 1125–1136 (2015)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Austria, part of Springer Nature 2018

Authors and Affiliations

  • Zhenzhou Wu
    • 1
  • Jianbing Zhu
    • 2
    • 3
  • Yan Chang
    • 1
  • Yajie Xu
    • 1
  • Xiaodong Yang
    • 1
    Email author
  1. 1.Medical Imaging DivisionSuzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhouChina
  2. 2.Medical Imaging DepartmentSuzhou Hospital Affiliated to Nanjing Medical UniversitySuzhouChina
  3. 3.Medical Imaging DepartmentSuzhou Science and Technology Town HospitalSuzhouChina

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