Bayesian speckle tracking. Part I: an implementable perturbation to the likelihood function for ultrasound displacement estimation

Abstract

Accurate and precise displacement estimation has been a hallmark of clinical ultrasound. Displacement estimation accuracy has largely been considered to be limited by the Cramer-Rao lower bound (CRLB). However, the CRLB only describes the minimum variance obtainable from unbiased estimators. Unbiased estimators are generally implemented using Bayes' theorem, which requires a likelihood function. The classic likelihood function for the displacement estimation problem is not discriminative and is difficult to implement for clinically relevant ultrasound with diffuse scattering. Because the classic likelihood function is not effective, a perturbation is proposed. The proposed likelihood function was evaluated and compared against the classic likelihood function by converting both to posterior probability density functions (PDFs) using a noninformative prior. Example results are reported for bulk motion simulations using a 6λ tracking kernel and 30 dB SNR for 1000 data realizations. The canonical likelihood function assigned the true displacement a mean probability of only 0.070 ± 0.020, whereas the new likelihood function assigned the true displacement a much higher probability of 0.22 ± 0.16. The new likelihood function shows improvements at least for bulk motion, acoustic radiation force induced motion, and compressive motion, and at least for SNRs greater than 10 dB and kernel lengths between 1.5 and 12λ.

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