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Novel Techniques In Ultrasonic Correlation Spectroscopy

Characterizing the Dynamics of Strongly Scattering Materials
  • M. L. Cowan
  • J. H. Page
  • D. A. Weitz
Part of the Acoustical Imaging book series (ACIM, volume 26)

Abstract

In strongly scattering materials, especially those containing a large concentration of high-contrast scattering objects (e.g. particles or inclusions), traditional ultrasonic imaging techniques break down, motivating the search for other approaches to characterize the properties of such materials. Even though acoustic wave scattering cannot readily be used to image the position of each scatterer unambiguously under these circumstances, much can be learned about the dynamic properties of the medium when the scatterers are moving. For particles suspended in a liquid, much has already been achieved through the development of sophisticated Doppler ultrasound imaging techniques, which have been extensively used to measure fluid flow when the scattering particles move together collectively throughout the scattering volume probed by the ultrasonic beam. However, there are many important cases where the motion is more complex, requiring new experimental approaches and methods of analysis to extract meaningful information from the scattered ultrasonic signals. To address this need, we have developed two ultrasonic correlation spectroscopies, Dynamic Sound Scattering (DSS) and Diffusing Acoustic Wave Spectroscopy (DAWS)1, which exploit the dynamic information present in ultrasonic speckles that are ubiquitous to wave scattering experiments. These techniques use correlation spectroscopy to measure the motion of the scatterers even in situations where the variance exceeds the mean velocity of the scatterers, thus greatly extending the type of dynamic information that can be obtained from ultrasonic scattering experiments.

Keywords

Speckle Pattern Diffusion Approximation Field Fluctuation Velocity Correlation Diffuse Wave Spectroscopy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media New York 2002

Authors and Affiliations

  • M. L. Cowan
    • 1
  • J. H. Page
    • 1
  • D. A. Weitz
    • 2
  1. 1.Department of Physics and AstronomyUniversity of ManitobaWinnipegCanada
  2. 2.Dept. of Physics and DEASHarvard UniversityCambridgeUSA

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