Abstract
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.
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Zong, Y., Li, B., Wan, M., Wang, S. (2006). Detection of Individual Microbubbles Using Wavelet Transform Based on a Theoretical Bubble Oscillation Model. In: Jiao, L., Wang, L., Gao, X., Liu, J., Wu, F. (eds) Advances in Natural Computation. ICNC 2006. Lecture Notes in Computer Science, vol 4222. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881223_44
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DOI: https://doi.org/10.1007/11881223_44
Publisher Name: Springer, Berlin, Heidelberg
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