Tissue Strain Estimation Using a Lagrangian Speckle Model
Accurate assessment of tissue motion from spatio-temporal changes in ultrasound speckles is a key factor in computing high signal-to-noise ratio (SNR) elastograms or correlation-based flow profiles. Provided the tissue (or fluid) is subjected to a simple translation movement, reliable sub-wavelength displacement estimates can be obtained through cross-correlation delay computation. On the other hand when the tissue is subjected to more complex movement, displacement estimates are generally found to be less accurate; indeed the changes in speckle patterns that result from such a motion act as the noise source often responsible for most of the displacement estimate variance. In this paper we propose a method to estimate 2-D motion while optimally compensating for speckle decorrelation. The method is based on a Lagrangian speckle model we proposed in
KeywordsPoint Spread Function Quantization Noise Inverse Filter Tissue Motion Displacement Estimate
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