Skip to Main Content
Ultrasound nonlinear imaging using microbubble- based contrast agents has been widely investigated. Nonetheless, its contrast is often reduced by the nonlinearity of acoustic wave propagation in tissue. In this paper, we explore the use of empirical mode decomposition (EMD) and ensemble empirical mode decomposition (EEMD) in the Hilbert-Huang transform (HHT) for possible contrast improvement. The HHT is designed for analyzing nonlinear and nonstationary data, whereas EMD is a method associated with the HHT that allows decomposition of data into a finite number of intrinsic modes. The hypothesis is that the nonlinear signal from microbubbles and the tissue nonlinear signal can be better differentiated with EMD and EEMD, thus making contrast improvement possible. Specifically, we tested this method on pulse-inversion nonlinear imaging, which is generally regarded as one of the most effective nonlinear imaging methods. The results show that the contrast-to-tissue ratios at the fundamental and second-harmonic frequencies were improved by 10.2 and 4.3 dB, respectively, after EEMD. Nonetheless, image artifacts also appeared, and hence further investigation is needed before EMD and EEMD can be applied in practical applications of ultrasound nonlinear imaging.