Advertisement

Clutter Filtering for Diagnostic Ultrasound Color Flow Imaging

  • D. V. LeonovEmail author
  • N. S. Kulberg
  • V. A. Fin
  • V. A. Podmoskovnaya
  • L. S. Ivanova
  • A. S. Shipaeva
  • A. V. Vladzimirskiy
  • S. P. Morozov
Article
  • 7 Downloads

Clutter filtering plays an important role in constructing a quality color flow map in ultrasound Doppler imaging. Signals from slow-moving tissues and vessel walls are clutter as they often mix with reflections from blood and should be suppressed for the further correct estimation of flow parameters. Their complete suppression in color flow imaging is difficult, because these signals on average are 40-60 dB more powerful than the signals from blood, the length of the Doppler sequence is very short, and there is always a demand for a real-time operation. This article provides a general model of the Doppler signal and discusses filters based on polynomial and adaptive regression, empirical mode decomposition, and prospective combined approaches to blood flow filtering.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Song, P., Manduca, A., Trzasko, J. D., and Chen, S., “Ultrasound small vessel imaging with block-wise adaptive local clutter filtering,” IEEE Trans. Med. Imag., 36, No. 1, 251-262 (2017).CrossRefGoogle Scholar
  2. 2.
    Li, Y. L., Hyun, D., Abou-Elkacem, L., Willmann, J. K., and Dahl, J. J., “Visualization of small-diameter vessels by reduction of incoherent reverberation with coherent flow power Doppler,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 63, No. 11, 1878-1889 (2016).CrossRefGoogle Scholar
  3. 3.
    Yu, A. C. H., Johnston, K. W., and Cobbold, R., S. C., “Frequency-based signal processing for ultrasound color flow imaging,” Canad. Acoust., 35, No. 2, 11-23 (2007).Google Scholar
  4. 4.
    Shen, Z., Feng, N., Shen, Y., and Lee, C. H., “An improved para-metric relaxation approach to blood flow signal estimation with single-ensemble in color flow imaging,” J. Med. Biomed. Eng., 33, No. 3, 309-318 (2013).Google Scholar
  5. 5.
    Torp, H., “Clutter rejection filters in color flow imaging: A theoretical approach,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 44, No. 2, 417-323 (1997).CrossRefGoogle Scholar
  6. 6.
    Yu, A. C. H. and Lovstakken, L., “Eigen-based clutter filter design for ultrasound color flow imaging: A review,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., No. 5, 1096 (2010).Google Scholar
  7. 7.
    Yu, A. C. H. and Cobbold, R. S. C., “Single-ensemble-based Eigen-processing methods for color flow imaging – Part, I., The Hankel-SVD Filter,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., No. 3, 559-572 (2008).Google Scholar
  8. 8.
    Yoo, Y. M., Managuli, R., and Kim, Y., “Adaptive clutter filtering for ultrasound color flow imaging,” Ultrasound Med. Biol., 29, No. 9, 1311-1320 (2003).CrossRefGoogle Scholar
  9. 9.
    Wang, P. D., Shen, Y., and Feng, N. Z., “A novel clutter rejection scheme in color flow imaging,” Ultrasonics, No. 44, Supplement 1, e303-e305 (2006).Google Scholar
  10. 10.
    Bjærum, S. and Torp, H., “Statistical evaluation of clutter filters in color flow imaging,” Ultrasonics, No. 38, 376-380 (2000).Google Scholar
  11. 11.
    Kargel, C., Höbenreich, G., Trummer, B., and Insana, M. F., “Adaptive clutter rejection filtering in ultrasonic strain-flow imaging, IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 50, No. 7, 824-835 (2003).CrossRefGoogle Scholar
  12. 12.
    Chee, A. J. and Alfred, C. H., “Receiver operating characteristic analysis of eigen-based clutter filters for ultrasound color flow imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 65, No. 3, 390-399 (2017).CrossRefGoogle Scholar
  13. 13.
    Chee, A. J., Yiu, B. Y., and Alfred, C. H., “A GPU-parallelized eigen-based clutter filter framework for ultrasound color flow imaging,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 64, No. 1, 150-163 (2017).CrossRefGoogle Scholar
  14. 14.
    Lovstakken, L., Signal Processing in Diagnostic Ultrasound: Algorithms for Real-Time Estimation and Visualization of Blood Flow Velocity, Doctoral Thesis, Norwegian University of Science and Technology (2007).Google Scholar
  15. 15.
    Shen, Z., Feng, N., and Shen, Y., “A forward-backward subsequence smoothing eigen-based approach to designing clutter rejection filters in color flow imaging,” IEEE Proc., 43, 535-538 (2014).Google Scholar
  16. 16.
    Park, G., Kim, Y., Shim, H., Koh, H. W., Lim, H., Lee, J. J., Yeo, S., Song, T. K., and Yoo, Y., “New adaptive clutter rejection based on spectral decomposition and tissue acceleration for ultrasound color Doppler imaging,” IEEE Ultrason. Symp., 1484-1487 (2014).Google Scholar
  17. 17.
    Park, G., Yeo, S., Lee, J. J., Yoon, C., Koh, H., Lim, H., Kim, Y., Shim, H., and Yoo, Y., “New adaptive clutter rejection based on spectral analysis for ultrasound color Doppler imaging: Phantom and in vivo abdominal study,” IEEE Trans. Biomed. Eng., 61, No. 1, 55-63 (2014).CrossRefGoogle Scholar
  18. 18.
    Kadi, A. and Loupas, T., “On the performance of regression and step-initialized IIR Clutter filters for color Doppler systems in diagnosing medical ultrasound,” IEEE Trans. Ultrason. Ferroelectr. Freq. Contr., 42, No. 5, 927-937 (1995).CrossRefGoogle Scholar
  19. 19.
    Khan, I. A., Hamid, E., and Nakai, T., “Systolic phase detection from pulsed Doppler ultrasound signal using EMD_DHT based approach,” Int. j. Signal Proc. Image. Proc. Pattern Recogn., 7, No. 5, 207-216 (2014).Google Scholar
  20. 20.
    Lo, M. T., Hu, K., Peng, C. K., and Novak, V., “Multimodal pressure flow analysis: application of Hilbert Huang transform in cerebral blood flow regulation,” EURASIP J., Adv. Signal Process., Article id: 785243 (2008).Google Scholar
  21. 21.
    Boronoev, B. B. and Omnokov, V. D., “Empirical mode decomposition of pulsed signals. Ground cover probing with radar and synthetic aperture radiometers,” MNTK (2013); http://ipms.bsc-net.ru/conferenc/RS2013/ru/docs/papers/a04.pdf.
  22. 22.
    Davydov, A. V., “The Hilbert-Huang transform,” http://geoin.org/hht (date accessed: June 1, 2018).
  23. 23.
    Shen, Z. and Lee, C. H., “LASSO based ensemble empirical mode decomposition approach to designing adaptive clutter suppression filters,” Proc. IEEE Acoust. Speech Signal Proc. (ICASSP), 757-760 (2012).Google Scholar
  24. 24.
    Gao, L., Zhang, Y., Lin, W., Li, H., Zhou, Y., Zhang, K., Li, Z., and Zhang, J., “A novel quadrature clutter rejection approach based on the multivariate empirical mode decomposition for bidirectional Doppler ultrasound signals,” Biomed. Signal Proc. Contr., 13, 31-40 (2014).CrossRefGoogle Scholar
  25. 25.
    Shen, Z., Feng, N., Shen, Y., and Lee, C. H., “A ridge ensemble empirical mode decomposition approach to clutter rejection for ultrasound color flow imaging,” IEEE Trans. Biomed. Eng., 60, No. 6, 1477-1487 (2013).CrossRefGoogle Scholar
  26. 26.
    Torres, S., Ground Clutter Cancelling with a Regression Filter, National Severe Storms Lab. Interim Report, Oklahoma, October 1998.Google Scholar
  27. 27.
    Zhou, X., Zhang, C., and Liu, D. C., “Adaptive clutter filter in 2D color flow imaging based on in vivo I/Q signal,” Biomed. Mater. Eng., 24, No. 1, 307-313 (2014).Google Scholar
  28. 28.
    Gerbands, J. J., “On the relationships between SVD, KLT and PCA,” Pattern Recognition, No. 14, 375-381 (1981).Google Scholar
  29. 29.
    Zobly, A. M. S. and Kadah, Y. M., “A new clutter rejection technique for Doppler ultrasound signal based on principal and independent component analyses,” in: Cairo International Biomedical Engineering Conference (CIBEC) (2012), pp. 56-59.Google Scholar
  30. 30.
    Baranger, J., Arnal, B., Perren, F., Baud, O., Tanter, M., and Demené, C., “Adaptive spatiotemporal SVD clutter filtering for Ultrafast Doppler Imaging using similarity of spatial singular vectors,” IEEE Trans. Med. Imaging, No. 37, 1574-1586 (2018).Google Scholar
  31. 31.
    Osipov, L. V., Kulberg, N. S., Leonov, D. V., and Morozov, S. P., “3D Ultrasound: Current State, Emerging Trends and Technologies,” Biomed. Eng., No. 3, 199-203 (2018).Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • D. V. Leonov
    • 1
    Email author
  • N. S. Kulberg
    • 1
  • V. A. Fin
    • 2
  • V. A. Podmoskovnaya
    • 2
  • L. S. Ivanova
    • 2
  • A. S. Shipaeva
    • 2
  • A. V. Vladzimirskiy
    • 1
  • S. P. Morozov
    • 1
  1. 1.Research and Practical Clinical Center of Diagnostics and Telemedicine Technologies, Department of Healthcare of MoscowMoscowRussia
  2. 2.National Research University “Moscow Power Engineering Institute”MoscowRussia

Personalised recommendations