臺灣博碩士論文加值系統

統計模型被廣泛應用於描述超音波逆散射包封訊號的機率密度函數，而超音波逆散射包封訊號的統計模型可定量分析生物組織特性。由於超音波能量的衰減會隨著頻率的增加而上升，導致回波訊號變形，影響統計參數的分析。因此本研究探討超音波頻率、激發週期與衰減效應對超音波逆散射包封訊號機率密度函數的影響。實驗使用3.5及7.5 MHz聚焦式換能器量測散射假體，散射假體為混合吉利丁與不同濃度的玻璃珠(直徑為30~50 μm)所製作，分別是16與 64 顆散射子/mm3；衰減效應是利用在換能器與散射假體之間擺放不同厚度的矽膠片分別為0、0.1、0.2與0.3 mm。超音波換能器分別以1、3、5與10個週期的弦波訊號激發，此研究顯示當以一個週期的弦波訊號激發換能器時，會使得Nakagami參數明顯受到衰減效應的影響。在頻寬的效應下，隨著激發週期的上升頻寬越趨近於窄頻而抑制衰減效應，尤以三個週期的弦波訊號可以保留較好的軸向解析度與有效抑制衰減效應。Nakagami參數隨著散射子濃度、激發的週期數、衰減效應的增加而有遞增的趨勢。

Statistical models, frequently adopted for describing the probability distribution function (PDF) of ultrasonic backscattered envelopes, have been utilized for tissue characterization. Due to the attenuation increase with ultrasonic frequency, the estimated statistical parameter can be affected by the broad-band attenuation in tissues and distortion of acquired echo signals. Hence, different ultrasonic frequencies and excitation signals were implemented to investigate the attenuation effect on statistical analysis of ultrasonic backscattered signals. Measurements were performed from tissue-mimicking phantoms, which were consisted of gelatin and glass beads with concentrations of 16 and 64 scatterers/mm3, using 3.5 and 7.5 MHz focus transducers. Per each experiment, a phantom was placed between the transducer and tissue-mimicking phantom for creating attenuation. Ultrasound signals were generated by exciting transducers with 1, 3, 5, and 10 cycles sinusoidal signals. The Nakagami statistical model was used to analyze the PDF of ultrasonic backscattered envelopes. This study further indicated that the attenuation could significantly vary the PDF of ultrasonic envelopes especially for the transducer was excited by monocycle sinusoidal signals. As a narrower bandwidth associated with the increase of cycles of tone burst was implemented, the attenuation effect was substantially reduced. The sinusoidal signals of 3 cycles among other excitation signals demonstrated the most appropriate performance to accommodate between attenuation effect and image resolution.

論文口試委員審定書(中文) I
論文口試委員審定書(英文) II
摘要 III
Abstract IV
誌謝 V
Table of Contents VI
List of Tables VIII
List of Figures IX
CHAPTER 1.Introduction 1
1.1 General 1
1.2 Ultrasonic tissue characterization 2
1.3 Research objectives 4
CHAPTER 2.Theoretical Background 6
2.1 Fundamentals of ultrasonic wave propagation 6
2.2 Reflection and refraction 8
2.3 Attenuation, absorption, and scattering 11
2.4 Ultrasonic transducers and sound field 16
2.5 Statistical models for ultrasonic backscattering 19
CHAPTER 3.Materials and Methods 27
3.1 Experiments on phantoms 27
3.2 Experimental arrangement 29
CHAPTER 4.Results and Discussion 38
4.1 Calibration of the attenuated phantom 38
4.2 Distributions of the ultrasonic backscattered envelopes 42
4.3 The statistical model of probability density distribution 55
4.4 Discussion 58
CHAPTER 5.Conclusions and Future Works 61
5.1 Conclusions 61
5.2 Future works 62
References 63

[1]K. K. Shung, Diagnostic Ultrasound: Imaging and Blood Flow Measurements: Taylor ＆ Francis, 2006.
[2]O. Bonnefous and P. Pesque, Time domain formulation of pulse-Doppler ultrasound and blood velocity estimation by cross correlation, Ultrason. Imaging, vol. 8, pp. 73-85, Apr 1986.
[3]D. D. Adler, P. L. Carson, J. M. Rubin, and D. Quinn-Reid, Doppler ultrasound color flow imaging in the study of breast cancer: preliminary findings, Ultrasound Med. Biol., vol. 16, pp. 553-9, 1990.
[4]J. W. Korstanje, R. W. Selles, H. J. Stam, S. E. Hovius, and J. G. Bosch, Development and validation of ultrasound speckle tracking to quantify tendon displacement, J. Biomech., vol. 43, pp. 1373-9, 2010.
[5]Y. Yoshii, H. R. Villarraga, J. Henderson, C. Zhao, K. N. An, and P. C. Amadio, Speckle tracking ultrasound for assessment of the relative motion of flexor tendon and subsynovial connective tissue in the human carpal tunnel, Ultrasound Med. Biol., vol. 35, pp. 1973-81, 2009.
[6]J. Ophir, I. Cespedes, H. Ponnekanti, Y. Yazdi, and X. Li, Elastography: a quantitative method for imaging the elasticity of biological tissues, Ultrason. Imaging, vol. 13, pp. 111-34, Apr 1991.
[7]TM Mak, YP Huang, YP Zheng, Liver Fibrosis Assessment Using Transient Elastography Guided with Real-time B-mode Ultrasound Imaging: A Feasibility Study, Ultrasound Med. Biol., 2013.
[8]D. Nicholas, D. K. Nassiri, P. Garbutt, and C. R. Hill, Tissue characterization from ultrasound B-scan data, Ultrasound Med. Biol., vol. 12, pp. 135-43, 1986.
[9]J. C. Bamber and C. R. Hill, Ultrasonic attenuation and propagation speed in mammalian tissues as a function of temperature, Ultrasound Med. Biol., vol. 5, pp. 149-57, 1979.
[10]E. R. Hughes, T. G. Leighton, G. W. Petley, and P. R. White, Ultrasonic propagation in cancellous bone: a new stratified model, Ultrasound Med. Biol., vol. 25, pp. 811-21, 1999.
[11]C. M. Moran, N. L. Bush, and J. C. Bamber, Ultrasonic propagation properties of excised human skin, Ultrasound Med. Biol., vol. 21, pp. 1177-90, 1995.
[12]K. A. Wear and B. S. Garra, Assessment of bone density using ultrasonic backscatter, Ultrasound Med. Biol., vol. 24, pp. 689-95, 1998.
[13]M. O'Donnell, D. Bauwens, J. W. Mimbs, and J. G. Miller, Broadband Integrated Backscatter: An Approach to Spatially Localized Tissue Characterization in Vivo, Proc. IEEE Ultrason. Symp., 1979, pp. 175-178.
[14]R. C. Molthen, P. M. Shankar, and J. M. Reid, Characterization of ultrasonic B-scans using non-Rayleigh statistics, Ultrasound Med. Biol., vol. 21, pp. 161-70, 1995.
[15]S. W. Smith, H. Lopez, and W. J. Bodine, Jr., Frequency independent ultrasound contrast-detail analysis, Ultrasound Med. Biol., vol. 11, pp. 467-77, 1985.
[16]R. F. Wagner, S. W. Smith, J. M. Sandrik, and H. Lopez, Statistics of Speckle in Ultrasound B-Scans, IEEE Trans. Sonics Ultrason., vol. 30, pp. 156-163, 1983.
[17]R. F. Wagner, M. F. Insana, and D. G. Brown, Statistical properties of radio-frequency and envelope-detected signals with applications to medical ultrasound J. Opt. Soc. Am. A-Opt., vol. 4, pp. 910-22, May 1987.
[18]P. M. Shankar, A model for ultrasonic scattering from tissues based on the K distribution, Phys. Med. Biol., vol. 40, pp. 1633-49, Oct 1995.
[19]P. M Shankar, A general statistical model for ultrasonic backscattering from tissues, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 47, pp. 727-736, 2000.
[20]J. C. Bamber and C. Daft, Adaptive filtering for reduction of speckle in ultrasonic pulse-echo images, Ultrasonics, vol. 24, pp. 41-4, 1986.
[21]R. Li, Z. Sun, and C. Zhang, Adaptive filter for speckle reduction with feature preservation in medical ultrasound images, IEEE Intl. Conf. on Control Auto., Rob. and Vision, pp. 1787-1792.
[22]Y. M. Kadah, A. A. Farag, J. M. Zurada, A. M. Badawi, and A. M. Youssef, Classification algorithms for quantitative tissue characterization of diffuse liver disease from ultrasound images, IEEE Trans. Med. Imaging, vol. 15, pp. 466-78, 1996.
[23]L. Weng, J. M. Reid, P. M. Shankar, and K. Soetanto, Ultrasound speckle analysis based on the K distribution, J. Acoust. Soc. Am., vol. 89, pp. 2992-5, Jun 1991.
[24]P. M. Shankar, V. A. Dumane, J. M. Reid, V. Genis, F. Forsberg, C. W. Piccoli, et al., Classification of ultrasonic B-mode images of breast masses using Nakagami distribution, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 48, pp. 569-580, 2001.
[25]J. A. Zagzebski, J. F. Chen, F. Dong, and T. Wilson, Intervening attenuation affects first-order statistical properties of ultrasound echo signals, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 46, pp. 35-40, 1999.
[26]K. U. I. Philip M. Morse, and R. S. Shankland, Theoretical Acoustics, Princeton University Press, 1968.
[27]V. Dutt and J. F. Greenleaf, Ultrasound echo envelope analysis using a homodyned K distribution signal model, Ultrason. Imaging, vol. 16, pp. 265-287, 1994.
[28]C. B. Burckhardt, Speckle in ultrasound B-mode scans, IEEE Trans. Sonics. Ultrason., vol. 25, pp. 1-6, 1978.
[29]R. C. Molthen, P. M. Shankar, J. M. Reid, F. Forsberg, E. J. Halpern, C. W. Piccoli, Comparisons of the Rayleigh and K-distribution models using in vivo breast and liver tissue, Ultrasound Med. Biol., vol. 24, pp. 93-100, 1998.
[30]B. I. Raju and M. A. Srinivasan, Statistics of envelope of high-frequency ultrasonic backscatter from human skin in vivo, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 49, pp. 871-882, 2002.
[31]V. M. Narayanan, P. M. Shankar, and J. M. Reid, Non-Rayleigh statistics of ultrasonic backscattered signals, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 41, pp. 845-852, 1994.
[32]E. L. Madsen, M. A. Hobson, H. Shi, T. Varghese, and G. R. Frank, Tissue-mimicking agar/gelatin materials for use in heterogeneous elastography phantoms, Phys. Med. Biol., vol. 50, pp. 5597-5618, 2005.
[33]E. L. Madsen, J. A. Zagzebski, R. A. Banjavie, and R. E. Jutila, Tissue mimicking materials for ultrasound phantoms, Med. Phys., vol. 5, pp. 391-4, 1978.
[34]K. Raum and W. D. O'Brien, Pulse-echo field distribution measurement technique for high-frequency ultrasound sources, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 44, pp. 810-815, 1997.
[35]D. J. McClements and P. Fairley, Ultrasonic pulse echo reflectometer, Ultrasonics, vol. 29, pp. 58-62, 1991.
[36]P. He, Acoustic attenuation estimation for soft tissue from ultrasound echo envelope peaks, IEEE Trans. Ultrason. Ferroelect. Freq. Control., vol. 36, pp. 197-203, 1989.
[37]H. J. Nieminen, S. Saarakkala, M. S. Laasanen, J. Hirvonen, J. S. Jurvelin, and J. Töyräs, Ultrasound attenuation in normal and spontaneously degenerated articular cartilage, Ultrasound Med. Biol., vol. 30, pp. 493-500, 2004.