Decomposition-Based Compression of Ultrasound Raw-Data

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9237)


Sonography techniques use multiple transducer elements for tissue visualization. The signals detected at each element are combined in the process of digital beamforming, requiring that large amounts of data be acquired, transferred and processed. One of the main challenges is reducing the data size while retaining the image contents. For this purpose, we propose a component based model for the raw ultrasonic signals. We show that a decomposition based approach with a suited processing scheme for each component individually, can achieve over twenty-fold reduction of needed data size.


Biomedical ultrasound Signal modeling Sparse representation Dictionary learning 


  1. 1.
    Aharon, M., Elad, M., Bruckstein, A.: K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation. IEEE Trans. Signal Proc. 54(11), 4311–4322 (2006)CrossRefGoogle Scholar
  2. 2.
    Azhari, H.: Basics of Biomedical Ultrasound for Engineers. Wiley, Hoboken (2010)CrossRefGoogle Scholar
  3. 3.
    Baraniuk, R.: Compressive radar imaging. In: Proceedings of the IEEE Radar Conference, pp. 128–133 (2007)Google Scholar
  4. 4.
    Basarab, A., Liebgott, H., Bernard, O., Friboulet, D., Kouame, D.: Medical ultrasound image reconstruction using distributed compressive sampling. In: IEEE 10th International Symposium on Biomedical Imaging (ISBI), pp. 628–631, April 2013Google Scholar
  5. 5.
    Bhattacharya, S., Blumensath, T., Mulgrew, B., Davies, M.: Fast encoding of synthetic aperture radar raw data using compressed sensing. In: IEEE Workshop on Statistical Signal Processing (2007)Google Scholar
  6. 6.
    Blu, T., Dragotti, P.L., Vetterli, M., Marziliano, P., Coulot, L.: Sparse sampling of signal innovations: theory, algorithms and performance bounds. IEEE Signal Process. Mag. 25(2), 31–40 (2008)CrossRefGoogle Scholar
  7. 7.
    Bohs, L.N., Trahey, G.E.: A novel method for angle independent ultrasonic imaging of blood flow and tissue motion. IEEE Trans. Biomed. Eng. 38(3), 280–286 (1991)CrossRefGoogle Scholar
  8. 8.
    Burckhardt, C.B.: Speckle in ultrasound B-mode scans. IEEE Trans. Sonics Ultrason. 25(1), 1–6 (1978)CrossRefGoogle Scholar
  9. 9.
    Candes, E.J., Wakin, M.B.: An introduction to compressive sampling. IEEE Signal Process. Mag. 25(2), 21–30 (2008)CrossRefGoogle Scholar
  10. 10.
    Donoho, D.L.: Compressed sensing. IEEE Trans. Inf. Theory 52(4), 1289–1306 (2006)MATHMathSciNetCrossRefGoogle Scholar
  11. 11.
    Friboulet, D., Liebgott, H., Prost, R.: Compressive sensing for raw RF signals reconstruction in ultrasound. In: IEEE Ultrasonics Symposium (IUS), pp. 367–370 (2010)Google Scholar
  12. 12.
    Li, Y.F., Li, P.C.: Ultrasound beamforming using compressed data. IEEE Trans. Inf. Technol. Biomed. 16(3), 308–313 (2012)CrossRefGoogle Scholar
  13. 13.
    Liebgott, H., Basarab, A., Kouame, D., Bernard, O., Friboulet, D.: Compressive sensing in medical ultrasound. In: IEEE International Ultrasonics Symposium (IUS), pp. 1–6 (2012)Google Scholar
  14. 14.
    Liebgott, H., Prost, R., Friboulet, D.: Pre-beamformed RF signal reconstruction in medical ultrasound using compressive sensing. Ultrasonics 53(2), 525–533 (2013)CrossRefGoogle Scholar
  15. 15.
    Lorintiu, O., Liebgott, H., Bernard, O., Friboulet, D.: Compressive sensing ultrasound imaging using overcomplete dictionaries. In: IEEE International Ultrasonics Symposium, pp. 45–48 (2013)Google Scholar
  16. 16.
    Lustig, M., Donoho, D.L., Santos, J.M., Pauly, J.M.: Compressed sensing MRI. In: IEEE Signal Processing Magazine (2007)Google Scholar
  17. 17.
    Patel, V.M., Easley, G.R., Healy, D.M., Chellappa, R.: Compressed synthetic aperture radar. IEEE J. Sel. Top. Signal Proces. 4(2), 244–254 (2010)CrossRefGoogle Scholar
  18. 18.
    Quinsac, C., Basarab, A., Girault, J.M., Kouame, D.: Compressed sensing of ultrasound images: sampling of spatial and frequency domains. In: IEEE Workshop on Signal Processing Systems (SiPS), Oct 2010Google Scholar
  19. 19.
    Quinsac, C., Basarab, A., Kouame, D.: Frequency domain compressive sampling for ultrasound imaging. Adv. Acoust. Vibr. 12, 1–16 (2012)CrossRefGoogle Scholar
  20. 20.
    Rilling, G., Davies, M., Mulgrew, B.: Compressed sensing based compression of SAR raw data. In: SPARS 2009 - Signal Processing with Adaptive Sparse Structured Representations, Saint Malo, France (2009)Google Scholar
  21. 21.
    Schiffner, M.F., Schmitz, G.: Fast pulse-echo ultrasound imaging employing compressive sensing. In: IEEE International Ultrasonics Symposium (IUS), pp. 688–691 (2011)Google Scholar
  22. 22.
    Szabo, T.L.: Diagnostic Ultrasound Imaging: Inside Out. Academic Press Series in Biomedical Engineering. Elsevier Academic Press, Burlington (2004)Google Scholar
  23. 23.
    Trahey, G.E., Allison, J.W., Von Ramm, O.T.: Angle independent ultrasonic detection of blood flow. IEEE Trans. Biomed. Eng. BME–34(12), 965–967 (1987)CrossRefGoogle Scholar
  24. 24.
    Tur, R., Eldar, Y.C., Friedman, Z.: Innovation rate sampling of pulse streams with application to ultrasound imaging. IEEE Trans. Signal Proc. 59(4), 1827–1842 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Vetterli, M., Marziliano, P., Blu, T.: Sampling signals with finite rate of innovation. IEEE Trans. Signal Proc. 50(6), 1417–1428 (2002)MathSciNetCrossRefGoogle Scholar
  26. 26.
    Wagner, N., Eldar, Y.C., Feuer, A., Friedman, Z.: Compressed beamforming applied to B-mode ultrasound imaging. In: Proceedings of the 9th IEEE International Symposium on Biomedical Imaging (ISBI) (2012)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Electrical EngineeringTechnion - I.I.THaifaIsrael

Personalised recommendations