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Using Empirical Mode Decomposition of Backscattered Ultrasound Signal Power Spectrum for Assessment of Tissue Compression

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Strony
447--453
Opis fizyczny
Bibliogr. 17 poz., rys., wykr.
Twórcy
autor
  • Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
autor
  • Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
autor
  • Department of Ultrasound, Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawińskiego 5B, 02-106 Warsaw, Poland
Bibliografia
  • 1. Byra M., Nowicki A., Piotrzkowska-Wróblewska H., Litniewski J., Dobruch-Sobczak K. (2015), Correcting the influence of tissue attenuation on Nakagami distribution shape parameter estimation, 2015 IEEE International Ultrasonics Symposium (IUS), Taipei, pp. 1-4, doi: 10.1109/ULTSYM.2015.0408.
  • 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), Livertissue 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 spectralbased 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), Twodimensional temperature estimation using diagnostic ultrasound, IEEE transactions on Ultrasonics, Ferroelectrics, and Frequency Control, 45, 4, 1088-1099.
Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-3b9bf7b1-1c06-480c-b0e8-039b8fd31978
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