Feasibility of using a generalized-Gaussian Markov random field prior for Bayesian speckle tracking of small displacements

Journal Article

Accurate displacement estimation can be a challenging task in acoustic radiation force elastography, where signal decorrelation can degrade the ability of a normalized cross-correlation (NCC) estimator to characterize the tissue response. In this work, we describe a Bayesian estimation scheme which models both signal decorrelation and thermal noise, and uses an edge-preserving, generalized Gaussian Markov random field prior. The performance of the estimator was evaluated in FEM simulations modeling the acoustic radiation force impulse response in a linearly-isotropic material. Bias, variance, and mean-square error were calculated over a range of estimator parameters, and compared to NCC. The results demonstrate that a significant reduction in mean-square error can be achieved with the proposed estimator. Finally, in vivo data of an radio-frequency ablation in a canine model are shown, demonstrating the in vivo feasibility of the proposed method.

Full Text

Duke Authors

Cited Authors

  • Dumont, D; Palmeri, M; Eyerly, S; Wolf, P; Byram, B

Published Date

  • October 20, 2014

Published In

Start / End Page

  • 1845 - 1848

Electronic International Standard Serial Number (EISSN)

  • 1948-5727

International Standard Serial Number (ISSN)

  • 1948-5719

Digital Object Identifier (DOI)

  • 10.1109/ULTSYM.2014.0458

Citation Source

  • Scopus