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10 September 1987 3-D Ultrasonic Speckle Modeling: Below The Rayleigh Limit
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Proceedings Volume 0768, Pattern Recognition and Acoustical Imaging; (1987)
Event: International Symposium on Pattern Recognition and Acoustical Imaging, 1987, Newport Beach, CA, United States
Ultrasonic speckle in B-scan images is usually modeled with simplifying assumptions about the propagation phenomenon. Moreover the resolution cell is supposed to contain a large number of scatterers. This assumption leads to a Rayleigh distribution for the image amplitude and more generally to speckle statistics which do not carry information about the scatterers distribution (Rayleigh limit). To take into account any propagation and diffraction effects we introduce a complex 3-dimensionnal point spread function which is not constrained to be space invariant. To deal with situations in which few scatterers are present in the resolution cell (low scatterer density or near-focus measurements) we establish the expressions of the first and second order statistical moments of the image intensity without the assumption of large scatterer density, i.e. below the Rayleigh limit. As a result, it appears an explicit dependence of the image contrast and of the speckle pattern autocovariance on the volumic scatterer density: the speckle carries information about the scattering medium. Of course, classical results are obtained by letting n tend towards infinity. The so-called SNR is shown to be inferior to its Rayleigh limit, depending on the product of scatterer density by the "effective volume": a defintion introduced to give an unambiguous (quantitative) measure of the volume of the resolution cell. The speckle spot size in both axial and transversal directions is also simply expressed as a function of the scatterer density.
© (1987) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Jean-Francois Cardoso "3-D Ultrasonic Speckle Modeling: Below The Rayleigh Limit", Proc. SPIE 0768, Pattern Recognition and Acoustical Imaging, (10 September 1987);

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