Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan.
IEEE Trans Ultrason Ferroelectr Freq Control. 2010;57(2):317-26. doi: 10.1109/TUFFC.2010.1412.
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.
基于微泡的超声非线性成像是目前研究的热点。然而，由于声波在组织中的非线性传播，其对比通常会降低。在本文中，我们探索了经验模态分解（EMD）和集合经验模态分解（EEMD）在希尔伯特-黄变换（HHT）中的应用，以提高可能的对比度。HHT 是为分析非线性和非平稳数据而设计的，而 EMD 是与 HHT 相关的一种方法，它允许将数据分解为有限数量的固有模式。假设微泡的非线性信号和组织的非线性信号可以通过 EMD 和 EEMD 更好地区分，从而实现对比度的提高。具体来说，我们在脉冲反转非线性成像中测试了这种方法，该方法通常被认为是最有效的非线性成像方法之一。结果表明，在经过 EEMD 后，基频和二次谐波的对比度与组织的比值分别提高了 10.2dB 和 4.3dB。然而，图像伪影也出现了，因此在 EMD 和 EEMD 应用于超声非线性成像的实际应用之前，还需要进一步的研究。
