Improvement of elastographic displacement estimation using a two-step cross-correlation method.

Abstract

The cross-correlation algorithm used to compute the local strain components for elastographic imaging requires a minimum radio-frequency data segment length of around 10 wavelengths to obtain accurate and precise strain estimates with a reasonable signal-to-noise ratio. Shorter radio-frequency data segments generally introduce increased estimation errors as the information content in the data segment reduces. However, shorter data segments and increased overlaps are essential to improve the axial resolution in the strain image. In this paper, we propose a two-step cross-correlation technique that enables the use of window lengths on the order of a single wavelength to provide displacement and strain estimates with similar noise properties as those obtained with a 10 wavelength window. The first processing step utilizes a window length on the order of 10 wavelengths to obtain coarse displacement estimates between the pre- and post-compression radio frequency data frames. This coarse displacement is then interpolated and utilized as the initial guess-estimate for the second cross-correlation processing step using the smaller window. This step utilizes a single wavelength window to improve the axial resolution in strain estimation, without significantly compromising the noise properties of the image. Simulation and experimental results show that the signal-to-noise and contrast-to-noise ratio estimates improve significantly at the smaller window lengths with the two-step processing when compared with the use of a similar sized window in the currently utilized single window method.