Detection of Individual Microbubbles Using Wavelet Transform Based on a Theoretical Bubble Oscillation Model

  • Yujin Zong
  • Bin Li
  • Mingxi Wan
  • Supin Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4222)


Detecting individual microbubbles is important for the quantification of the amount of bubbles in the tissues, determination of microvascular volume and targeted microbubble imaging. We took the advantage of a theoretical bubble oscillation model to construct a matched wavelet, i.e. bubble wavelet as mother wavelet to detect individual microbubble using wavelet transform. The experimental echoes with different levels of added noises were processed. The results showed significant improvement even for an Echo-Noise-Ratio (ENR in ) of -20 dB and the spatial location demonstrated very close agreement with the original experimental echo. This technique was much better than those based on harmonic analysis especially under the circumstance of short pulse insonation.


Wavelet Transform Wavelet Coefficient Mother Wavelet Continuous Wavelet Transform Matched Filter 


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  1. 1.
    Leighton, T.G.: The Acoustic Bubble. Academic Press, London (1994)Google Scholar
  2. 2.
    Miller, D.L., Williams, A.R., Gross, D.R.: Ultrasonic detection of resonant cavitation bubbles in a flow tube by their second-harmonic emissions. Ultrasonics 22, 217–224 (1984)Google Scholar
  3. 3.
    Leighton, T.G., Ramble, D.G., Phelps, A.D.: The detection of tethered and rising bubbles using multiple acoustic techniques. J. Acoust. Soc. Am. 101, 2626–2635 (1997)CrossRefGoogle Scholar
  4. 4.
    de Jong, N.: Acoustic properties of ultrasound contrast agents. PhD Dissertation Erasmus University, Rotterdam (1993)Google Scholar
  5. 5.
    Goldberg, B.B., Raichlen, J.S., Forsberg, B.: Ultrasound contrast agents: basic principles and clinical applications, 2nd edn. Martin Dunitz, London (2001)Google Scholar
  6. 6.
    de Jong, N., Frinking, P.A., Bouakaz, A., Cate, F.T.: Detection procedures of ultrasound contrast agents. Ultrasonics 38, 87–92 (2000)CrossRefGoogle Scholar
  7. 7.
    Unger, E., Matsunaga, T.O., Schumann, P.A., Tutsi, R.: Microbubbles in molecular imaging and therapy. Medicamundi 47, 58–65 (2003)Google Scholar
  8. 8.
    Lamerichs, R., Schäffter, T., Hämisch, Y., Powers, J.: Molecular Imaging: the road to better healthcare. Medicamundi 47, 2–9 (2003)Google Scholar
  9. 9.
    Klibanov, A.L., Rasche, P.T., Hughes, M.S., et al.: Detection of Individual Microbubbles of an Ultrasound Contrast Agent: Fundamental and Pulse Inversion Imaging. Acad. Radiol. 9, S279–S281 (2002)CrossRefGoogle Scholar
  10. 10.
    Droste, D.W., Silling, K., Stypmann, K.J., et al.: Contrast Transcranial Doppler Ultrasound in the Detection of Right-to-Left Shunts: Time Window and Threshold in Microbubble Numbers. Stroke 9, 1640–1645 (1999)Google Scholar
  11. 11.
    Droste, D.W., Lakemieier, S., Wichter, T., Stypmann, J., et al.: Optimizing the Technique of Contrast Tran-scranial Doppler Ultrasound in the Detection of Right-to-Left Shunts. Stroke 9, 2211–2216 (2002)CrossRefGoogle Scholar
  12. 12.
    Zheng, W., Newhouse, V.L.: Onset delay of acoustic second harmonic backscatter from bubbles or microspheres. Ultrasound Med. Biol. 24, 513–522 (1998)CrossRefGoogle Scholar
  13. 13.
    Shi, W.T., Forsberg, F., Raichlen, J.S., Needleman, L., Goldberg, B.B.: Pressure dependence of subharmonic signals from contrast microbubbles. Ultrasound Med. Biol. 25, 275–283 (1999)CrossRefGoogle Scholar
  14. 14.
    Bouakaz, A., Frigstad, S., Folkert, J., Cate, T., de Jong, D.: Super harmonic imaging: A new imaging technique for improved contrast detection. Ultrasound Med. Biol. 28, 59–68 (2002)CrossRefGoogle Scholar
  15. 15.
    Frisch, M., Messer, H.: Detection of a known transient signal of unknown scaling and arrival time. Process IEEE Trans. sign. process. 42, 1859–1863 (1994)CrossRefGoogle Scholar
  16. 16.
    Allen, J.S., May, D.J., Ferrara, K.W.: Dynamics of therapeutic ultrasound contrast agents. Ultrasound Med. Biol. 28, 805–816 (2002)CrossRefGoogle Scholar
  17. 17.
    Morgan, K.E., Allen, J.S., Dayton, P.A., Chomas, J.E., Klibanov, A.L., Ferrara, K.W.: Experimental and theoretical evaluation of microbubble behavior: Effect of transmitted phase and bubble size. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 47, 1494–1509 (2000)CrossRefGoogle Scholar
  18. 18.
    Dayton, P.A.: The effects of acoustic radiation force on contrast agents: Experimental and theoretical analysis. PhD Dissertation (2001)Google Scholar
  19. 19.
    Daubechies, I.: Ten lectures on wavelets. SIAM, Philadelphia (1992)MATHGoogle Scholar
  20. 20.
    Du, Y.F., Wan, M.X., Wang, S.P., et al.: Surfactant-based nano-shelled microbubble ultrasound contrast agent. Journal of Chemical Industry and Engineering 54, 807–812 (2003)Google Scholar
  21. 21.
    Abbate, A., Koay, J., Frankel, J., Schroeder, S.C., Das, P.: Signal detection and noise suppression using a wavelet transform signal processor: Application to ultrasonic flaw detection. IEEE Trans. Ultrason. Ferroelectr. Freq. Control 44, 14–26 (1997)CrossRefGoogle Scholar
  22. 22.
    Mallat, S., Hwang, W.L.: Singularity detection and processing with wavelet. IEEE Trans. Information theory 38, 617–638 (1992)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yujin Zong
    • 1
  • Bin Li
    • 1
  • Mingxi Wan
    • 1
  • Supin Wang
    • 1
  1. 1.Department of Biomedical engineering, Key Laboratory of Biomedical Information Engineering of Ministry of EducationXi’an Jiaotong UniversityXi’anChina

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