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A spectral-based method for tissue characterization

Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Quantitative ultrasound methods are widely investigated as a promising tool for tissue characterization. In this paper, a novel quantitative method is developed which can be used to assess scattering properties of tissues. The proposed method is based on analysis of oscillations of the backscattered echo power spectrum. It is shown that these oscillations of the power spectrum are connected with the distances between scatterers within the medium. Two techniques are proposed to assess the scatterer’s distribution. First, we show that the inverse Fourier transform of the backscattered echo power spectrum corresponds to a histogram of the distances between scatterers. Second, the Hilbert-Huang transform is used to directly extract the power spectrum oscillations. Both methods are examined by means of a numerical experiment. A cellular gas model of a biological medium is considered. Results are presented and discussed. Both methods can be used to evaluate the scatterer’s distribution by means of the power spectrum oscillations.
Czasopismo
Rocznik
Tom
Strony
369--375
Opis fizyczny
Bibliogr. 15 poz., rys.
Twórcy
autor
  • Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences Pawińskiego 5B, Warsaw, Poland
autor
  • Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences Pawińskiego 5B, Warsaw, Poland
autor
  • Ultrasound Department, Institute of Fundamental Technological Research, Polish Academy of Sciences Pawińskiego 5B, Warsaw, Poland
Bibliografia
  • [1] J. Mamou, M.L. Oelze, Quantitative Ultrasound in Soft Tissues, Springer Netherlands, 2013.
  • [2] M.L. Oelze, J. Mamou, Review of Quantitative Ultrasound: Envelope Statistics and Backscatter Coefficient Imaging and Contributions to Diagnostic Ultrasound, IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 63 (2016) 336–351.
  • [3] M. Byra, B. Gambin, Temperature detection based on nonparametric statistics of ultrasound echoes, Hydroacoustics. 18 (2015) 17–23.
  • [4] A. Nowicki, H. Piotrzkowska-Wroblewska, J. Litniewski, M. Byra, B. Gambin, E. Kruglenko, K. Dobruch-Sobczak, Differentiation of normal tissue and tissue lesions using statistical properties of backscattered ultrasound in breast, in: Ultrason. Symp. (IUS), 2015 IEEE Int., 2015: pp. 1–4.
  • [5] H. Tadayyon, A. Sadeghi-Naini, G.J. Czarnota, Non-invasive characterization of locally advanced breast cancer using textural analysis of quantitative ultrasound parametric images, Transl. Oncol. 7 (2014) 759–767.
  • [6] H. Tadayyon, A. Sadeghi-Naini, L. Wirtzfeld, F.C. Wright, G. Czarnota, Quantitative ultrasound characterization of locally advanced breast cancer by estimation of its scatterer properties., Med. Phys. 41 (2014) 12903.
  • [7] P.H. Tsui, C.K. Yeh, Y.Y. Liao, C.C. Chang, W.H. Kuo, K.J. Chang, C.N. Chen, Ultrasonic Nakagami Imaging: A Strategy to Visualize the Scatterer Properties of Benign and Malignant Breast Tumors, Ultrasound Med. Biol. 36 (2010) 209–217.
  • [8] M. Byra, A. Nowicki, H. Piotrzkowska-Wróblewska, K. Dobruch-Sobczak, Classification of breast lesions using segmented quantitative ultrasound maps of homodyned K distribution parameters, Med. Phys. 43 (2016) 5561–5569.
  • [9] M. Byra, A. Nowicki, H. Piotrzkowska-Wroblewska, J. Litniewski, K. DobruchSobczak, Correcting the influence of tissue attenuation on Nakagami distribution shape parameter estimation, in: Ultrason. Symp. (IUS), 2015 IEEE Int., 2015: pp. 1–4.
  • [10] F.L. Lizzi, E.J. Feleppa, S. Kaisar Alam, C.X. Deng, Ultrasonic spectrum analysis for tissue evaluation, Pattern Recognit. Lett. 24 (2003) 637–658.
  • [11] R. van Sloun, L. Demi, C. Shan, M. Mischi, Ultrasound coefficient of nonlinearity imaging, IEEE Trans. Ultrason. Ferroelectr. Freq. Control. 62 (2015) 1331–1341.
  • [12] A. Nowicki, M. Byra, J. Litniewski, J. Wójcik, Two frequencies push-pull differential imaging, in: 2014 IEEE Int. Ultrason. Symp., 2014: pp. 710–713.
  • [13] S. Granchi, E. Vannacci, E. Biagi, L. Masotti, Multidimensional spectral analysis of the ultrasonic radiofrequency signal for characterization of media, Ultrasonics. 68 (2016)
  • [14] N. Uniyal, H. Eskandari, P. Abolmaesumi, S. Sojoudi, P. Gordon, L. Warren, R.N. Rohling, S.E. Salcudean, M. Moradi, Ultrasound RF time series for classification of breast lesions, IEEE Trans. Med. Imaging. 34 (2015) 652–661.
  • [15] N.E. Huang, Z. Shen, S.R. Long, M.C. Wu, H.H. Shih, Q. Zheng, N.-C. Yen, C.C. Tung, H.H. Liu, The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, Proc. R. Soc. A Math. Phys. Eng. Sci. 454 (1998) 903–995.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b367190e-b447-4c21-a012-86d313dd4e12
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