An adaptive displacement estimation algorithm for improved reconstruction of thermal strain

Abstract

Thermal strain imaging (TSI) can be used to differentiate between lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust displacement estimation over a wide range of displacement magnitudes. Phase-shift estimators such as Loupas’ estimator and time-shift estimators like normalized cross-correlation (NXcorr) are commonly used to track tissue displacements. However, Loupas’ estimator is limited by phase-wrapping and NXcorr performs poorly when the signal-to-noise ratio (SNR) is low. In this paper, we present an adaptive displacement estimation algorithm that combines both Loupas’ estimator and NXcorr. We evaluated this algorithm using computer simulations and an ex-vivo human tissue sample. Using 1-D simulation studies, we showed that when the displacement magnitude induced by thermal strain was >λ/8 and the electronic system SNR was >25.5 dB, the NXcorr displacement estimate was less biased than the estimate found using Loupas’ estimator. On the other hand, when the displacement magnitude was ≤λ/4 and the electronic system SNR was ≤25.5 dB, Loupas’ estimator had less variance than NXcorr. We used these findings to design an adaptive displacement estimation algorithm. Computer simulations of TSI using Field II showed that the adaptive displacement estimator was less biased than either Loupas’ estimator or NXcorr. Strain reconstructed from the adaptive displacement estimates improved the strain SNR by 43.7–350% and the spatial accuracy by 1.2–23.0% (p < 0.001). An ex-vivo human tissue study provided results that were comparable to computer simulations. The results of this study showed that a novel displacement estimation algorithm, which combines two different displacement estimators, yielded improved displacement estimation and results in improved strain reconstruction.

Viscoelastic Biomarkers of Ex Vivo Liver Samples via Torsional Wave Elastography

“…The sequence of the figure (from A to D) shows the localization of the ARF push and the shear wave lateral propagation away from the focus. Loupas 2D autocorrelator performs as the gold standard phase domain technique for motion estimation [41,42].…”

Improved Estimation of Ultrasound Thermal Strain Using Pulse Inversion Harmonic Imaging

“…Both TSI and NIT have been tested using in vivo animal models and ex vivo tissue preparations (Ding et al 2015; Kim et al 2008; Lai et al 2010; Liu et al 2008; Mahmoud et al 2014; Miller et al 2004). For TSI, these studies have aimed at identifying the lipid rich core of atherosclerotic plaques or quantifying hepatic steatosis.…”