Bayesian Shear Wave Speed Reconstruction with an On-Axis ARFI Prior

Conference Paper

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.

Full Text

Duke Authors

Cited Authors

  • Chan, DY; Rouze, NC; Palmeri, ML; Nightingale, KR

Published Date

  • October 1, 2019

Published In

Volume / Issue

  • 2019-October /

Start / End Page

  • 213 - 216

Electronic International Standard Serial Number (EISSN)

  • 1948-5727

International Standard Serial Number (ISSN)

  • 1948-5719

International Standard Book Number 13 (ISBN-13)

  • 9781728145969

Digital Object Identifier (DOI)

  • 10.1109/ULTSYM.2019.8925845

Citation Source

  • Scopus