Bayesian regularization applied to ultrasound strain imaging.
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.
University ofWisconsin-Madison, Madison, WI 53706, USA. matt@mmmccormick.com
Matthew
M
McCormick
Nicholas
N
Rubert
Tomy
T
Varghese
D000465:Algorithms; D000818:Animals; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D002339:Carotid Arteries / Q000000981:diagnostic imaging; D003198:Computer Simulation; D054459:Elasticity Imaging Techniques / Q000379:methods*; D005260:Female; D006801:Humans; D008099:Liver / Q000000981:diagnostic imaging; D019047:Phantoms, Imaging; D012815:Signal Processing, Computer-Assisted; D013552:Swine
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