Bayesian regularization applied to ultrasound strain imaging

Matthew McCormick*, Nicholas Rubert, Tomy Varghese

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

48 Scopus citations


Noise artifacts due to signal decorrelation and reverberation are a considerable problem in ultrasound strain imaging. For block-matching methods, information from neighboring matching blocks has been utilized to regularize the estimated displacements. We apply a recursive Bayesian regularization algorithm developed by Hayton et al. [Artif. Intell., vol. 114, pp. 125-156, 1999] to phase-sensitive ultrasound RF signals to improve displacement estimation. The parameter of regularization is reformulated, and its meaning examined in the context of strain imaging. Tissue-mimicking experimental phantoms and RF data incorporating finite-element models for the tissue deformation and frequency-domain ultrasound simulations are used to compute the optimal parameter with respect to nominal strain and algorithmic iterations. The optimal strain regularization parameter was found to be twice the nominal strain and did not vary significantly with algorithmic iterations. The technique demonstrates superior performance over median filtering in noise reduction at strains 5 and higher for all quantitative experiments performed. For example, the strain SNR was 11 dB higher than that obtained using a median filter at 7 strain. It has to be noted that for applied deformations lower than 1, since signal decorrelation errors are minimal, using this approach may degrade the displacement image.

Original languageEnglish (US)
Article number5688295
Pages (from-to)1612-1620
Number of pages9
JournalIEEE Transactions on Biomedical Engineering
Issue number6
StatePublished - Jun 2011


  • Bayes procedures
  • biomedical acoustic imaging
  • biomedical imaging
  • displacement measurement
  • image motion analysis
  • strain measurement

ASJC Scopus subject areas

  • Biomedical Engineering


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