Preliminary results of attenuation estimation from tissue backscatter using commercial ultrasonic scanner

• Autorzy: Klimonda Z. , Litniewski J. , Nowicki A.
• Języki publikacji: EN

Abstrakty

EN

Ultrasonography (USG) is a widespread and powerful tool used successfully in modern diagnostics. The standard USG scanner reflects impedance variations within the tissue that is penetrated by the ultrasound pulse. Although such image provides a lot of information to the physician, there are another parameters which could be imaged. The attenuation coefficient is one of them. Imaging of attenuation seems to be a promising tool for ultrasonic medical diagnostics. The attenuation estimation method based on the echoes mean frequency changes due to tissue attenuation dispersion is presented. The Doppler IQ technique is adopted to estimate the mean frequency changes directly from the raw RF data. The Singular Spectrum Analysis (SSA) technique is used for the mean frequency trend extraction. The changes of the mean frequency trend are related directly to the local attenuation coefficient. Preliminary results of the tissue phantom attenuation coefficient estimation and imaging using the commercial scanner are presented.

• Wydawca: Polskie Towarzystwo Akustyczne. Oddział Gdański
• Czasopismo: Hydroacoustics
• Rocznik: 2010
• Tom: Vol. 13
• Strony: 127--134
• Opis fizyczny: Bibliogr. 22 poz., rys.

Twórcy

autor

Klimonda Z.

autor

Litniewski J.

autor

Nowicki A.

• Institute of Fundamental Technical Research, Polish Academy of Sciences Pawińskiego 5B, 02-106 Warsaw, Poland,

Bibliografia

• [1] B. J.Oosterveld, J.M. Thijssen, P.C. Hartman, R.L. Romijn, G. Josenbusch, Ultrasound attenuation and texture analysis of diffuse liver disease: methods and preliminary results, Phys. Med. Biol., Vol. 36(8), 1039–1064, 1991.
• [2] Y. Saijo, High Frequency Acoustic Properties of Tumor Tissue. In: Ultrasonic Tissue Characterization, Springer-Verlag Tokio, 217-230, Hong-Kong 1996.
• [3] T.A. Bigelow, B.L. Mcfarlin, W. D O’brien., M.L. Oelze, In vivo ultrasonic attenuation slope estimates for detectiong cervical ripening in rats: Preliminary results, Journal of Acoustical Society of America, Vol. 123(3), 1794-1800, 2008.
• [4] Z.F. Lu, J. Zagzebski, F.T. Lee, Ultrasound Backscatter and Attenuation in Human Liver With Diffuse Disease, Ultrasound in Med. & Biol., Vol. 25(7), 1047-1054, 1999.
• [5] H. J. Nieminen, S. Saarakkala, M.S. Laasanen, J. Hirvonen, J. S. Jurvelin, J. Töyräs Ultrasound Attenuation in Normal and Spontaneously Degenerated Articular Cartilage, Ultrasound in Med. & Biol., Vol. 30(4), 493-500, 2004.
• [6] V. Zderic, A. Keshavarzi, A.M. Andrew, S. Vaezy, R.W. Martin, Attenuation of Porcine Tissues In Vivo After High Intensity Ultrasound Treatment, Ultrasound in Med. & Biol., Vol. 30(1), 61-66, 2004.
• [7] A.E. Worthington, M.D. Sherar, Changes in Ultrasound Properties of Porcine Kidney Tissue During Heating, Ultrasound in Med. & Biol., Vol. 27(5), 673-682,2001.
• [8] Z. Klimonda, A. Nowicki, Imaging of the mean frequency of the ultrasonic echoes, Archives of Acoustics, Vol. 32(4) (supplement), 77-80, 2007.
• [9] J. Ophir, M.A. Ghouse, L.A. Ferrari, Attenuation estimation with the zero crossing technique: phantom studies, Ultras. Imag., Vol. 7, 122-132, 1985.
• [10] A. Nowicki, Ultrasonic Diagnostics [in Polish: Diagnostyka Ultradźwiękowa], MAKmed, 2000c
• [11] P. Laugier, G. Berger, M. Fink, J. Perrin, Specular reflector noise: effect and correction for in vivo attenuation estimation, Ultras. Imag. Vol. 7, 277-292, 1985.
• [12] J. Litniewski, Assessment of trabecular bone structure deterioration by ultrasound [in Polish: Wykorzystanie fal ultradźwiękowych do oceny zmian struktury kości gąbczastej], Prace IPPT, 2006.
• [13] A. Nowicki, Fundamentals of Doppler Ultrasonography [in Polish: Podstawy Ultrasonografii Dopplerowskiej], PWN, 1995.
• [14] F. J. Alonso, J. M. Del Castillo, P. Pintado, Application of singular spectrum analysis to the smoothing of raw kinematic signals.,Journal of Biomechanics, Vol. 38, 1085-1092, 2005.
• [15] N. E. Golyandina, K. D. Usevich, I. V. Florinsky, Filtering of Digital Terrain Models by Two-Dimensional Singular Spectrum Analysis, International Journal of Ecology & Development, Vol. 8(f07), 81-94, 2007.
• [16] H.Hassani, Singular Spectrum Analysis: Methodology and Comparison, Journal of Data Science, Vol. 5, 239-257, 2007.
• [17] J. C. Moore, A. Grinsted, Signular spectrum analysis and envelope detection: methods of enhancing the utility of ground-penetrating radar data, Journal of Glaciology, Vol. 52(176), 2006.
• [18] D.H. Schoellhamer, Singular spectrum analysis for time series with missing data, Geophysical Research Letters, Vol. 28(16), 3187-3190, 2001.
• [19] F. Varadi, R. K. Ulrich, L. Bertello, C. J. Henney, Random lag singular cross-spectrum analysis, The Astrophysical Journal, Vol. 528(1), 2000.
• [20] R. Vautard, P. Yiou, M. Ghil, Singular-spectrum analysis: a toolkit for short, noisy chaotic signals, Physica D, Vol. 58, 95-126, 1992.
• [21] T. Alexandrov, A Method of Trend Extraction using Singular Spectrum Analysis, REVSTAT Statistical Journal, Vol. 7(1), 1-22, 2009.
• [22] N. Golyandina, V. Nekrutkin, A. Ahigljavsky, Analysis of time Series Structure: SSA and related techniques, Chapman & Hall/CRC, 2001.