Adaptive displacement estimation for optimal reconstruction of thermal strain

Thermal strain imaging (TSI) can be used to differentiate lipid and water-based tissues in atherosclerotic arteries. However, detecting small lipid pools in vivo requires accurate and robust estimation of displacement 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' algorithm is limited by phase-wrapping and NXcorr performs poorly in low SNR situations. In this paper, we present an adaptive displacement estimation algorithm that showed performance that was superior to either Loupas' estimator or NXcorr alone when the displacement estimates were used to reconstruct thermal strain. We evaluated this algorithm using simulation and phantom studies.