Abstract
Multidimensional, high-resolution ultrasonic imaging of rapidly movingtissue is primarily limited by sparse sampling in the lateral dimension. Inorder to achieve acceptable spatial resolution and velocity quantization,interpolation of laterally sampled data is necessary. We present a novelmethod for estimating lateral subsample speckle motion and compare it withtraditional interpolation methods. This method, called grid slopes,requires no a priori knowledge and can be applied to data with asfew as two samples in the lateral dimension. Computer simulations wereperformed to compare grid slopes with two conventional interpolationschemes, parabolic fit and cubic spline. Results of computer simulationsshow that parabolic fit and cubic spline performed poorly at translationsgreater than 0.5 samples, and translations less than 0.5 samples weresubject to an estimation bias. Grid slopes accurately estimatedtranslations between 0 and 1 samples without estimation bias at highsignal-to-noise ratios. Given that the grid slopes interpolation techniqueperforms well at high signal-to-noise ratios, one pertinent clinicalapplication might be tissue motion tracking.
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