  In share wave elastography (SWE), the aim is to measure the velocity of shear
waves, however unwanted compression waves and bulk tissue motion pose
challenges in evaluating tissue stiffness. Conventional approaches often
struggle to discriminate between shear and compression waves, leading to
inaccurate shear wave speed (SWS) estimation. In this study, we propose a novel
approach known as the difference autocorrelation estimator to accurately
estimate reverberant SWS in the presence of compression waves and noise.
Methods: The difference autocorrelation estimator, unlike conventional
techniques, computes the subtraction of velocity between neighboring particles,
effectively minimizing the impact of long wavelength compression waves and
other wide-area movements such as those caused by respiration. We evaluated the
effectiveness of the integrated difference autocorrelation (IDA) by: (1) using
k-Wave simulations of a branching cylinder in a soft background, (2) using
ultrasound elastography on a breast phantom, (3) using ultrasound elastography
in the human liver-kidney region, and (4) using magnetic resonance elastography
(MRE) on a brain phantom. Results: By applying IDA on the unfiltered
contaminated wave fields of simulation and elastography experiments, the
estimated SWSs are in good agreement with the ground truth values (i.e., less
than 2% error for the simulation, 9% error for ultrasound elastography of
breast phantom and 19% error for MRE). Conclusion: Our results demonstrate that
IDA accurately estimates SWS, revealing the existence of a lesion, even in the
presence of strong compression waves. Significance: IDA exhibits consistency in
SWS estimation across different modalities and excitation scenarios,
highlighting its robustness and potential clinical utility.
