Correlation of ultrasonic scatterer size estimates for the statistical analysis and optimization of angular compounding.

Ultrasonic scatterer size estimates generally have large variances due to the inherent noise of spectral estimates used to calculate size. Compounding partially correlated size estimates associated with the same tissue, but produced with data acquired from different angles of incidence, is an effective way to reduce the variance without making dramatic sacrifices in spatial resolution. This work derives theoretical approximations for the correlation between these size estimates, and the coherence between their associated spectral estimates, as functions of ultrasonic system parameters. A Gaussian spatial autocorrelation function is assumed to adequately model scatterer shape. Both approximations compare favorably with simulation results, which consider validation near the focus. Utilization of the correlation/coherence expressions for statistical analysis and optimization is discussed. Approximations, such as the invariance of phase and amplitude terms with angle, are made to obtain closed-form solutions to the derived spectral coherence near the focus and permit analytical optimization analysis. Results indicate that recommended parameter adjustments for performance improvement generally depend upon whether, for the system under consideration, the primary source of change in total coherence with rotation is phase term variation due to the change in the relative position of scattering sites, or field amplitude term variation due to beam movement.

[1]  T. Varghese,et al.  Improved parametric imaging of scatterer size estimates using angular compounding , 2004, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[2]  T. Varghese,et al.  Statistics of ultrasonic scatterer size estimation with a reference phantom. , 2003, The Journal of the Acoustical Society of America.

[3]  William D O'Brien,et al.  Parametric Imaging of Rat Mammary Tumors In Vivo for the Purposes of Tissue Characterization , 2002, Journal of ultrasound in medicine : official journal of the American Institute of Ultrasound in Medicine.

[4]  William D O'Brien,et al.  Characterization of tissue microstructure using ultrasonic backscatter: theory and technique for optimization using a Gaussian form factor. , 2002, The Journal of the Acoustical Society of America.

[5]  B A Porter,et al.  Real-time spatial compound imaging: application to breast, vascular, and musculoskeletal ultrasound. , 2001, Seminars in ultrasound, CT, and MR.

[6]  E L Madsen,et al.  Estimating the spatial autocorrelation function for ultrasound scatterers in isotropic media. , 1998, Medical physics.

[7]  E. Feleppa,et al.  Statistics of ultrasonic spectral parameters for prostate and liver examinations , 1997, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[8]  M. Insana,et al.  Error bounds on ultrasonic scatterer size estimates. , 1996, The Journal of the Acoustical Society of America.

[9]  T. Hall,et al.  Renal Ultrasound Using Parametric Imaging Techniques to Detect Changes in Microstructure and Function , 1993, Investigative radiology.

[10]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences, 2nd ed. , 1993 .

[11]  R. F. Wagner,et al.  Statistics of Speckle in Ultrasound B-Scans , 1983, IEEE Transactions on Sonics and Ultrasonics.

[12]  P. R. Bevington,et al.  Data Reduction and Error Analysis for the Physical Sciences , 1969 .

[13]  Yadong Li,et al.  A frequency domain model for generating B-mode images with array transducers , 1999, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[14]  E. Feleppa,et al.  Statistical framework for ultrasonic spectral parameter imaging. , 1997, Ultrasound in medicine & biology.

[15]  M. Insana Ultrasonic imaging of microscopic structures in living organs. , 1996, International review of experimental pathology.

[16]  G G Cox,et al.  Ultrasonic measurement of glomerular diameters in normal adult humans. , 1996, Ultrasound in medicine & biology.

[17]  M. Insana,et al.  Modeling acoustic backscatter from kidney microstructure using an anisotropic correlation function. , 1995, The Journal of the Acoustical Society of America.

[18]  T J Hall,et al.  Identifying acoustic scattering sources in normal renal parenchyma in vivo by varying arterial and ureteral pressures. , 1992, Ultrasound in medicine & biology.

[19]  T J Hall,et al.  Identifying acoustic scattering sources in normal renal parenchyma from the anisotropy in acoustic properties. , 1991, Ultrasound in medicine & biology.

[20]  R. F. Wagner,et al.  Describing small-scale structure in random media using pulse-echo ultrasound. , 1990, The Journal of the Acoustical Society of America.

[21]  R. L. Romijn,et al.  Estimation of scatterer size from backscattered ultrasound: a simulation study , 1989, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[22]  R. F. Wagner,et al.  Fundamental correlation lengths of coherent speckle in medical ultrasonic images , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[23]  S. D. Silverstein,et al.  Optimum displacement for compound image generation in medical ultrasound , 1988, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[24]  S.W. Smith,et al.  Speckle Pattern Correlation with Lateral Aperture Translation: Experimental Results and Implications for Spatial Compounding , 1986, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.