Investigation of parametric spectral estimation techniques for elasticity imaging

Kenneth Hoyt, Flemming Forsberg, Jonathan Ophir

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Several autoregressive (AR) and autoregressive moving average (ARMA) parametric spectral estimators were evaluated for use in tissue strain estimation. Using both 1-D simulations and in vitro phantom experiments, the performance of these parametric spectral strain estimators were compared against both a nonparametric discrete Fourier transform (DFT) spectral strain estimator and a coherent elastographic technique. Parametric spectral estimator model orders were selected based on a modified strain filter approach. This technique illustrated the trade-offs between different signal-processing parameters and a strain estimator performance measure, namely the area under the strain filter (using applied strain dynamic range of 0.1 to 50%). The Yule-Walker AR spectral strain estimator outperformed all other parametric methods evaluated, but failed to outperform the DFT-based approach. Furthermore, both these spectral strain-estimation techniques exhibit an elastographic signal-to-noise ratio (SNRe) and strain estimation dynamic range not achievable using conventional elastography without global stretching.

Original languageEnglish (US)
Pages (from-to)1109-1121
Number of pages13
JournalUltrasound in Medicine and Biology
Issue number8
StatePublished - Aug 2005


  • Elasticity imaging
  • Elastography
  • Parametric models
  • Spectral estimation
  • Spectral strain imaging

ASJC Scopus subject areas

  • Biophysics
  • Radiological and Ultrasound Technology
  • Acoustics and Ultrasonics


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