Skip to main content
SpringerLink
Log in

A Low-complexity Minimum Variance Algorithm Combined with Power Method for Ultrasound Imaging

  • ACOUSTIC SIGNALS PROCESSING. COMPUTER SIMULATION
  • Published:
Acoustical Physics Aims and scope Submit manuscript

Abstract

Aiming at the problem of high complexity and poor real-time performance of the traditional minimum variance (MV) algorithm, a low-complexity minimum variance algorithm combined with power method is proposed. Firstly, the echo data is transformed into beam domain by discrete cosine transform and the dimension reduction parameter is determined according to the data of scanning lines. Secondly, the maximum eigenvalue and corresponding eigenvector of sample covariance matrix are obtained by the power method to reduce the complexity of eigenvalue decomposition. Finally, by ignoring low-energy echo signal, the inversion of covariance matrix can be simplified to construct a new weighted vector, which can reduce the complexity of MV. The Field II simulation results show that the proposed algorithm has better resolution, contrast ratio and efficiency than the traditional MV algorithm, and outperforms the minimum variance algorithm based on eigenvalue decomposition (ESBMV) in resolution and efficiency.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.

Similar content being viewed by others

REFERENCES

  1. L. V. De, G. Szekely, and C. Tanner, Ultrasound Med. Biol. 41 (12), 3044 (2018).

    Google Scholar 

  2. R. S. Bandaru, A. R. Sornes, J. Hermans, E. Samset, and J. D’hooge, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 63 (12), 2057 (2016).

    Article  Google Scholar 

  3. M. Sorensen, I. Domanov, and L. L. De, IEEE Trans. Signal Process. 66 (14), 3665 (2018).

    Article  ADS  MathSciNet  Google Scholar 

  4. A. Hassanien and S. A. Vorobyov, IEEE Signal Process. Lett. 16 (1), 22 (2009).

    Article  ADS  Google Scholar 

  5. C. C. Zheng, J. Cheng, and H. Peng, Acta Acust. 42 (1), 109 (2017).

    Google Scholar 

  6. J. Kortbek, J. A. Jensen, and K. L. Gammelmark, Ultrasonics 53 (1), 1 (2013).

    Article  Google Scholar 

  7. C. C. Shen, Y. Q. Xing, and G. Jeng, Ultrasonics 72 (1), 177 (2016).

    Article  Google Scholar 

  8. C. C. Zheng, H. Peng, and Z. H. Han, Acta Phys. Sin. 63 (14), 413 (2014).

    Google Scholar 

  9. L. N. Ribeiro, A. L. F. de Almeida, and J. C. M. Mota, Signal Process. 158 (1), 15 (2019).

    Article  Google Scholar 

  10. P. Wang, Y. Z. Shi, J. Y. Jiang, L. Kong, and Z. H. Gong, Acoust. Phys. 65 (1), 123 (2019).

    Article  ADS  Google Scholar 

  11. T. Dietzen, A. Spriet, W. Tirry, S. Doclo, M. Moonen, and W. T. Van, IEEE/ACM Trans. Audio Speech Lang. Process. 27 (3), 544 (2019).

    Article  Google Scholar 

  12. P. Wang, Z. H. Gong, N. Cheng, and N. Li, Acta Acust. 42 (2), 214 (2017).

    ADS  Google Scholar 

  13. I. K. Holfort, F. Gran, and J. Jeonsen, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 56 (2), 314 (2009).

    Article  Google Scholar 

  14. P. Wang, N. Cheng, Z. H. Gong, and L. H. Wang, Acta Phys. Sin. 64 (23), 404 (2015).

    Google Scholar 

  15. J. K. Li, X. D. Chen, Y. Wang, Y. F. Shi, and D. Y. Yu, Acoust. Phys. 63 (2), 229 (2017).

    Article  ADS  Google Scholar 

  16. T. Su, S. Zhang, D. Y. Li, and D. J. Yao, Acoust. Phys. 64 (3), 379 (2018).

    Article  ADS  Google Scholar 

  17. Q. Wang, B. Zhou, Y. Chen, and H. Quan, Acoust. Phys. 65 (2), 226 (2019).

    Article  ADS  Google Scholar 

  18. D. R. Fuhrmann, IEEE Trans. Signal Process. 39 (10), 2194 (1991).

    Article  ADS  Google Scholar 

  19. J. Park, S. M. Wi, and J. S. Lee, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 63 (2), 256 (2016).

    Article  Google Scholar 

  20. K. Kim, S. Park, J. Kim, S. B. Park, and M. Bae, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 61 (6), 930 (2014).

    Article  Google Scholar 

  21. J. Yang, E. Grunsky, and Q. M. Cheng, Nat. Resour. Res. 28 (2), 431 (2019).

    Article  Google Scholar 

  22. R. C. de Lamare and R. Sampaio-Neto, IEEE Trans. Veh. Technol. 59 (3), 1217 (2010).

    Article  Google Scholar 

  23. M. Albulayli and D. Rakhmatov, in Proc. 2013 IEEE Int. Conference on Acoustics, Speech, and Signal Processing (ICASSP) (Vancouver, 2013), p. 1061.

  24. A. M. Deylami and B. M. Asl, Ultrasonics 66 (1), 43 (2016).

    Article  Google Scholar 

  25. L. Candemir and I. Cilesiz, in Proc. 2008 Medical Imaging 2008: Physics of Medical Imaging(SPIE) (San Diego, CA, 2008), p. 6913.

    Google Scholar 

  26. K. Ranganathan and W. F. Walker, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 54 (4), 782 (2007).

    Article  Google Scholar 

  27. J. C. Tillett, J. P. Astheimer, and R. C. Waag, IEEE Trans. Ultrason., Ferroelectr. Freq. Control 57 (1), 214 (2010).

    Article  Google Scholar 

Download references

Funding

The work is funded by the National Key Research and the Development Program no. 2018YFB2100100, the NSFC Grant no. 51 677 010, Science and Technology Research Program of Chongqing Municipal Education Commission no. KJQN201803102 and Chongqing Natural Science Foundation no. cstc2018jcyjAX0032.

Author information

Authors and Affiliations

  1. State Key Lab. of Power Transmission Equip. and System Security and New Tech., Chongqing University, 400044, Chongqing, China

    Ping Wang, Tingting Du, Lu Kong & Xitao Li

  2. Chongqing College of Electronic Engineering, 401331, Chongqing, China

    Linhong Wang

  3. State Grid Fujian Electric Power Co., Ltd., Maintenance Branch, 350003, Fujian, China

    Yizhe Shi

Authors
  1. Ping Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tingting Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Linhong Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lu Kong
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Xitao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Yizhe Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ping Wang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ping Wang, Du, T., Wang, L. et al. A Low-complexity Minimum Variance Algorithm Combined with Power Method for Ultrasound Imaging. Acoust. Phys. 66, 204–212 (2020). https://doi.org/10.1134/S1063771020020074

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S1063771020020074

Keywords:

Search

Navigation