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.