Bayesian Shear Wave Speed Reconstruction with an On-Axis ARFI Prior
Shear wave elasticity image quality can be degraded by poor signal-to-noise ratio or spatial resolution due to low shear wave amplitudes and reconstruction kernel size. A framework is presented for incorporating additional information about relative stiffness, based on the on-axis ARFI displacement data, for enhanced shear wave speed reconstruction. Using Bayes' theorem, a prior distribution describing the expected shear wave speed based on local displacement magnitudes is combined with a likelihood function, which describes the estimated speed based on the tracked shear wave data. In a phantom, the Bayesian estimator increased range of reconstructed depths by 55% compared to a conventional cross-correlation SWEI method, and decreased SWS bias compared to the ARFI-only reconstruction. The Bayesian estimator also improved visualization of in vivo prostate anatomy and prostate cancer.
