New Developments in Ultrasonic Tissue Characterization

  • Frederic L. Lizzi
Part of the Acoustical Imaging book series (ACIM, volume 19)

Abstract

Ultrasonic tissue characterization (UTC) encompasses a broad range of signal processing techniques applied to video or radio-frequency (rf) echo signals.1 In our laboratories, we have investigated several rf-signal analysis techniques that are based on calibrated power spectra.2 To obtain summary spectral parameters, we have used linear regression analysis to determine spectral slopes, spectral intercepts, and residual intercept uncertainties (measures of the goodness-of-fit to the spectrum). In ophthalmology, we have employed these features in more than 2,000 examinations, and we have established clinical data-bases to diagnose and monitor ocular tumors.3,4 As part of these studies, discriminant analysis has been used to classify and sub-classify malignant melanomas, metastatic carcinomas, and choroidal hemangiomas. For abdominal 5 and vascular 6 examinations, we have developed means for using these spectral parameters to compute additional UTC features (e.g., attenuation and heterogeneity indices) that are not affected by attenuation in intervening tissues. We have also developed a mathematical framework 7 that relates spectral parameters to physical scatterer properties.

Keywords

Attenuation Retina Autocorrelation Acoustics Hemangioma 

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References

  1. 1.
    Greenleaf, J.F. (Ed.), Characterization of Tissue with Ultrasound,CRC Press, Boca Raton, 1986.Google Scholar
  2. 2.
    Lizzi, F.L., Greenebaum, M., Feleppa, E.J., Elbaum, M. and Coleman, D.J., “Theoretical Framework for Spectrum Analysis in Ultrasonic Tissue Characterization,” J. Acoust. Soc. Am., 73: 1366–1373, 1983.ADSCrossRefGoogle Scholar
  3. 3.
    Feleppa, E.J., Lizzi, F.L. and Coleman, D.J., “Ultrasonic Analysis of Ocular Tumor Characterization and Therapy Assessment,” Int. Union Physiol. Sci./Am. Physiol. Sc., NIPS, 3: 193–197, 1988.Google Scholar
  4. 4.
    Coleman, D.J., Silverman, R.H., Rondeau, M.J., Lizzi, F.L., McLean, I.W and Jackobiec, F., “Correlations of Acoustic Tissue Typing of Malignant Melanoma and Histopathologic Features as a Predictor of Death,” Am. J. Ophthal. 110: 380–388, 1990.Google Scholar
  5. 5.
    King, D.L., Lizzi, F.L., Feleppa, E.J., Wai, P., Yaremko, M.M., Rorke, M.C. and Herbst, J., “Focal and Diffuse Liver Disease Studied by Quantitative Microstructural Sonography,” Radiology, 155: 457–462, 1985.Google Scholar
  6. 6.
    Sigel, B., Feleppa, E.J., Swami, V., Justin, J., Consigny, M., Machi, J., Kikuchi, T., Lizzi, F.L., Kurohiji, T. and Hui, J., “Ultrasonic Tissue Characterization of Blood Clots,” Surgical Clinics of North America, 70: 13–29, 1990.Google Scholar
  7. 7.
    Lizzi, F.L., Ostromogilsky, M., Feleppa, E.J., Rorke, M.C., and Yaremko, M.M., “Relationship of Ultrasonic Spectral Parameters to Features of Tissue Microstructure,” IEEE Trans. on Ultrasonics, Ferroelectrics, and Frequency Control, UFFC-34: 319–329, 1987.Google Scholar
  8. 8.
    Lizzi, F.L., King, D.L., Rorke, M.C., Hui, J., Ostromogilsky, M., Yaremko, M.M., Feleppa, E.J., and Wai, P., “Comparison of Theoretical Scattering Results and Ultrasonic Data from Clinical Liver Examinations,” Ultrasound in Med. & Biol., 14: 377–385, 1988.CrossRefGoogle Scholar
  9. 9.
    Feleppa, E.J., Lizzi, F.L., Coleman, D.J., and Yaremko, M.M., “Diagnostic Spectrum Analysis in Ophthalmology: A Physical Perspective,” Ultrasound in Med. & Biol., 12: 623–631, 1986.CrossRefGoogle Scholar
  10. 10.
    Kessler, L. W., “Imaging with Dynamic-ripple Diffraction,” in Acoustical Imaging, G. Wade, Ed., New York: Plenum, Ch. 10, pp. 229–239, 1976.Google Scholar
  11. 11.
    Embree, P.M., Kalervo, M.U., Foster, S.G., and O’Brien, W.D., “Spatial Distribution of the Speed of Sound in Biological Materials with the Scanning Laser Acoustic Microscope,” IEEE Trans. on Son. and Ultrason. SU-32, 341–350, 1985.CrossRefGoogle Scholar
  12. 12.
    Wagner, R.F., Insana, M.F., and Brown, D.G., “Unified Approach to the Detection and Classification of Speckle Texture in Diagnostic Ultrasound,” Opt. Eng. 25: 738–742, 1986.CrossRefGoogle Scholar
  13. 13.
    Tuthill, T.A., Sperry, R.H., and Parker, K.J., “Deviations from Rayleigh Statistics in Ultrasonic Speckle,” Ultrasonic Imaging 10, 81–89, 1988CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 1992

Authors and Affiliations

  • Frederic L. Lizzi
    • 1
  1. 1.Riverside Research InstituteNew YorkUSA

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