The effects of digitization on the elastographic signal-to-noise ratio.

In elastography, the tissue under investigation is compressed and the resulting strain is estimated from the gradient of the displacement (time-delay) estimates. The displacements are typically estimated by cross-correlating the radiofrequency (RF) ultrasound signals of the pre- and postcompressed tissue. One of the parameters used to quantify the resulting quality of the elastogram is the elastographic signal-to-noise ratio (SNR(e)). For a uniformly elastic target (a single elastic modulus), the dependence of the SNR(e) on the applied strain has a bandpass characteristic that has been termed the strain filter. Theoretical expressions for the upper bound on the strain filter were developed earlier. Yet, simulated as well as experimental strain filters derived from uniformly elastic phantoms deviate from these upper bounds. The failure to achieve the upper bounds could be partially attributed to the fact that, in both simulations and experiments, the RF signals used to compute the TDEs are sampled and quantized. Using simulated models of uniformly elastic phantoms, a study of the dependence of the strain filter on the quantization and sampling rates was performed. The results indicated that the strain filter improves with both the sampling rate and the quantization, as expected. A theoretical analysis was done to incorporate quantization as a derating factor to the strain filter.

[1]  F. Kallel,et al.  Elastography: A systems approach , 1997, Int. J. Imaging Syst. Technol..

[2]  G E Trahey,et al.  Properties of acoustical speckle in the presence of phase aberration. Part I: First order statistics. , 1988, Ultrasonic imaging.

[3]  T. Varghese,et al.  A theoretical framework for performance characterization of elastography: the strain filter , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[4]  T. Varghese,et al.  Enhancement of echo-signal correlation in elastography using temporal stretching , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[5]  J Ophir,et al.  The nonstationary strain filter in elastography: Part II. Lateral and elevational decorrelation. , 1997, Ultrasound in medicine & biology.

[6]  J. Ophir,et al.  Elastography: A Quantitative Method for Imaging the Elasticity of Biological Tissues , 1991, Ultrasonic imaging.

[7]  G. Carter Coherence and time delay estimation , 1987, Proceedings of the IEEE.

[8]  Julius S. Bendat,et al.  Random Data - Analysis and Measurement Procedures - Second Edition (revised and expanded) , 1986 .

[9]  J Ophir,et al.  Phase aberration effects in elastography. , 2001, Ultrasound in medicine & biology.

[10]  T. Krouskop,et al.  Elastography: Ultrasonic estimation and imaging of the elastic properties of tissues , 1999, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine.

[11]  T. Hall,et al.  2-D companding for noise reduction in strain imaging , 1998, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[12]  J. Ophir,et al.  Theoretical bounds on strain estimation in elastography , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[13]  J. Meunier,et al.  Ultrasonic biomechanical strain gauge based on speckle tracking , 1989, Proceedings., IEEE Ultrasonics Symposium,.

[14]  A. Weiss,et al.  Fundamental limitations in passive time delay estimation--Part I: Narrow-band systems , 1983 .

[15]  A. Quazi An overview on the time delay estimate in active and passive systems for target localization , 1981 .

[16]  W. Walker,et al.  A fundamental limit on delay estimation using partially correlated speckle signals , 1995, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[17]  J Ophir,et al.  The nonstationary strain filter in elastography: Part I. Frequency dependent attenuation. , 1997, Ultrasound in medicine & biology.

[18]  R. F. Wagner,et al.  Properties of Acoustical Speckle in the Presence of Phase Aberration Part II: Correlation Lengths , 1988, Ultrasonic imaging.

[19]  J. Ophir,et al.  Reduction of Image Noise in Elastography , 1993 .

[20]  J. Ophir,et al.  The combined effect of signal decorrelation and random noise on the variance of time delay estimation , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[21]  W. Tranter,et al.  Signals and Systems: Continuous and Discrete , 1983 .

[22]  J. Ophir,et al.  An adaptive strain estimator for elastography , 1998, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[23]  H. Smalley The systems approach. , 1972, Hospitals.

[24]  B. Garra,et al.  Elastography: Ultrasonic imaging of tissue strain and elastic modulus in vivo , 1996 .

[25]  T. Varghese,et al.  Direct strain estimation in elastography using spectral cross-correlation. , 2000, Ultrasound in medicine & biology.

[26]  G. Carter,et al.  The generalized correlation method for estimation of time delay , 1976 .

[27]  J. Meunier,et al.  Echographic image mean gray level changes with tissue dynamics: a system-based model study , 1995, IEEE Transactions on Biomedical Engineering.

[28]  M. O’Donnell,et al.  Measurement of arterial wall motion using Fourier based speckle tracking algorithms , 1991, IEEE 1991 Ultrasonics Symposium,.

[29]  T. Wilson,et al.  Intervening attenuation affects first-order statistical properties of ultrasound echo signals , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[30]  Michel Bertrand,et al.  Ultrasonic texture motion analysis: theory and simulation , 1995, IEEE Trans. Medical Imaging.

[31]  J. Ophir,et al.  Methods for estimation of subsample time delays of digitized echo signals. , 1995, Ultrasonic imaging.

[32]  J Ophir,et al.  Estimating the elastographic signal-to-noise ratio using correlation coefficients. , 2002, Ultrasound in medicine & biology.