Investigation of parametric spectral estimation techniques for elasticity imaging.
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 (SNR(e)) and strain estimation dynamic range not achievable using conventional elastography without global stretching.
Hoyt
Kenneth
K
Department of Radiology, Thomas Jefferson University, Philadelphia, PA, USA.
Forsberg
Flemming
F
Ophir
Jonathan
J
D000465:Algorithms; D001696:Biomechanical Phenomena; D003198:Computer Simulation; D004548:Elasticity; D005583:Fourier Analysis; D006801:Humans; D008433:Mathematics; D008954:Models, Biological; D019047:Phantoms, Imaging; D014159:Transducers; D014463:Ultrasonography / Q000379:methods*