Using Empirical Mode Decomposition of Backscattered Ultrasound Signal Power Spectrum for Assessment of Tissue Compression
Abstract
Quantitative ultrasound has been widely used for tissue characterization. In this paper we propose a new approach for tissue compression assessment. The proposed method employs the relation between the tissue scatterers’ local spatial distribution and the resulting frequency power spectrum of the backscattered ultrasonic signal. We show that due to spatial distribution of the scatterers, the power spectrum exhibits characteristic variations. These variations can be extracted using the empirical mode decomposition and analyzed. Validation of our approach is performed by simulations and in-vitro experiments using a tissue sample under compression. The scatterers in the compressed tissue sample approach each other and consequently, the power spectrum of the backscattered signal is modified. We present how to assess this phenomenon with our method. The proposed in this paper approach is general and may provide useful information on tissue scattering properties.Keywords:
tissue characterization, tissue compression, quantitative ultrasound, empirical mode decomposition, signal analysisReferences
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2. Byra M., Kruglenko E., Gambin B., Nowicki A. (2017), Temperature monitoring during focused ultrasound treatment by means of the homodyned K distribution, Acta Physica Polonica A, 131, 6, 1525–1528.
3. Curiale A.H., Vegas-Sanchez-Ferrero G., Aja-Fernandez S. (2017), Influence of ultrasound speckle tracking strategies for motion and strain estimation, Medical Image Analysis, 32, 184–200.
4. Dandel M., Lehmkuhl H., Knosalla C., Suramelashvili N., Hetzer R. (2009), Strain and strain rate imaging by echocardiography-basic concepts and clinical applicability, Current Cardiology Reviews, 5, 2, 133–148.
5. Daubechies I., Lu J., Wu H.T. (2011), Synchrosqueezed wavelet transforms: An empirical mode decomposition-like tool, Applied and Computational Harmonic Analysis, 30, 2, 243–261.
6. Dragomiretskiy K., Zosso D. (2014), Variational mode decomposition, IEEE Transactions on Signal Processing, 62, 531–544.
7. Ghoshal G., Kemmerer J.P., Karunakaran C., Miller R.J., Oelze M. (2016), Quantitative ultrasound for monitoring high-intensity focused ultrasound treatment in vivo, IEEE transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 63, 9, 1234–1242.
8. Huang N. E., et al. (1998), The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454, 1971, 903–995.
9. Lizzi F., Feleppa E., Jaremko N. (1981), Liver-tissue characterization by digital spectrum and cepstrum analysis, [In:] 1981 Ultrasonics Symposium, pp. 575–578.
10. Mamou J. Oelze M. (2013), Quantitative ultrasound in soft tissues, Springer, Netherlands.
11. Oelze M. Mamou J. (2016), Review of quantitative ultrasound: envelope statistics and backscatter coefficient imaging and contributions to diagnostic ultrasound, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 63, 2, 336–351.
12. Shung K. (1993), Ultrasonic scattering in biological tissues, Wiley, New York.
13. Tsui P.H., et al. (2017), Small-window parametric imaging based on information entropy for ultrasound tissue characterization, Scientific Reports, 7.
14. Wójcik J., Byra M., Nowicki A. (2016), A spectral-based method for tissue characterization, Hydroacoustics, 19, 369–375.
15. Yu X., Guo Y., Huang S.M., Li M.L., Lee W.N. (2015), Beamforming effects on generalized Nakagami imaging, Physics in medicine and biology, 60, 19, 7513..
16. Zhou Z., Wu W., Wu S., Jia K., Tsui P.H. (2017), A Review of Ultrasound Tissue Characterization with Mean Scatterer Spacing, Ultrasonic Imaging, 39, 5, 263-282.
17. Simon C., VanBaren P., Ebbini E.S. (1998), Two-dimensional temperature estimation using diagnostic ultrasound, IEEE transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 45, 4, 1088–1099.
