Complete and accurate separation of harmonic components from the ultrasonic radio frequency (RF) echo signals is essential to improve the quality of harmonic imaging. There are limitations in the existing two commonly used separation methods, that is, the subjectivity for the high-pass filtering (S_HPF) method and motion artifacts for the pulse inversion (S_PI) method. A novel separation method called S_CEEMDAN, based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm, is proposed to adaptively separate the second harmonic components for ultrasound tissue harmonic imaging. First, the ensemble size of the CEEMDAN algorithm is calculated adaptively according to the standard deviation of the added white noise. A set of intrinsic mode functions (IMFs) is then obtained by the CEEMDAN algorithm from the ultrasonic RF echo signals. According to the IMF spectra, the IMFs that contain both fundamental and harmonic components are further decomposed. The separation process is performed until all the obtained IMFs have been divided into either fundamental or harmonic categories. Finally, the fundamental and harmonic RF echo signals are obtained from the accumulations of signals from these two categories, respectively. In simulation experiments based on CREANUIS, the S_CEEMDAN-based results are similar to the S_HPF-based results, but better than the S_PI-based results. For the dynamic carotid artery measurements, the contrasts, contrast-to-noise ratios (CNRs), and tissue-to-clutter ratios (TCRs) of the harmonic images based on the S_CEEMDAN are averagely increased by 31.43% and 50.82%, 18.96% and 10.83%, as well as 34.23% and 44.18%, respectively, compared with those based on the S_HPF and S_PI methods. In conclusion, the S_CEEMDAN method provides improved harmonic images owing to its good adaptivity and lower motion artifacts, and is thus a potential alternative to the current methods for ultrasonic harmonic imaging.

中文翻译：

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基于改进的集合经验模式分解算法的自适应超声组织谐波成像

从超声射频 (RF) 回波信号中完全准确地分离谐波分量对于提高谐波成像质量至关重要。现有两种常用的分离方法存在局限性，即高通滤波（S_HPF）方法的主观性和脉冲反转（S_PI）方法的运动伪影。基于自适应噪声的完全集成经验模态分解（CEEMDAN）算法，提出了一种称为S_CEEMDAN的新分离方法，用于自适应分离超声组织谐波成像的二次谐波分量。首先，根据加入的白噪声的标准偏差自适应计算CEEMDAN算法的集成大小。然后通过 CEEMDAN 算法从超声波射频回波信号中获得一组固有模式函数 (IMF)。根据 IMF 谱，包含基波和谐波分量的 IMF 被进一步分解。执行分离过程，直到所有获得的 IMF 都被分为基波或谐波类别。最后，分别从这两类信号的累积中获得基波和谐波射频回波信号。在基于CREANUIS的仿真实验中，基于S_CEEMDAN的结果与基于S_HPF的结果相似，但优于基于S_PI的结果。对于动态颈动脉测量，对比度、对比度噪声比 (CNR)、基于 S_CEEMDAN 的谐波图像的组织杂波比（TCR）平均分别提高了 31.43% 和 50.82%、18.96% 和 10.83%，以及 34.23% 和 44.18%。 S_HPF 和 S_PI 方法。总之，S_CEEMDAN 方法由于其良好的适应性和较低的运动伪影提供了改进的谐波图像，因此是当前超声谐波成像方法的潜在替代方案。