Han-Oh S
Johns Hopkins University, Baltimore, MD.
Med Phys. 2012 Jun;39(6Part8):3685. doi: 10.1118/1.4734979.
We introduce a novel technique for analyzing tumor-motion surrogate signals using Empirical Mode Decomposition (EMD) and Hilbert-Huang Transformation (HHT).
The tumor-motion surrogate signals were acquired (with RPM/Varian), from 20 lung-cancer patients in free-breathing method and its data were decomposed into Intrinsic Mode Functions (IMFs) using EMD. HHT was then applied to each IMF to obtain instantaneous frequency as a function of time. The Result of the frequency information was compared to Fast Fourier Transformation (FFT) and manual calculation of frequency. Correlation of each IMF with the surrogate signal was used to determine the adequate IMF as a faithful tumor-motion surrogate.
The surrogate RPM signals were decomposed to 10 ± 1 IMFs on average. The decomposed IMFs showed three categories of frequencies: (1) high frequencies (1 - 10 Hz) such as a noise-like signal, (2) medium frequencies (0.1 - 0.3 Hz), which is potentially a true breathing signal, and (3) low frequencies (0.003 - 0.09 Hz), which behave a baseline drift. The marginal frequency, which is a measure of total amplitude contribution from each frequency, showed an average difference of -0.03 ± 0.07 from the FFT and -0.02 ± 0.05 with the manual calculations. Each surrogate signal showed a high correlation with one IMF (0.747 on average) and, a low correlation with the rest of the IMFs (0.139 on average). The IMF with a high correlation alone represented the surrogate signal well in terms of breathing frequency and amplitude.
The EMD and HHT were used to analyze the cyclic components of nonlinear and non-stationary surrogate signals in the time domain. Since the EMD decomposes the signal into physically-meaningful modes, it was possible to determine IMFs that represent the tumor motion faithfully after removing noise-like signals. Further investigation on physical meanings of the IMFs is the next step of the study.
我们介绍一种使用经验模态分解（EMD）和希尔伯特-黄变换（HHT）分析肿瘤运动替代信号的新技术。
从20例肺癌患者中以自由呼吸方式获取肿瘤运动替代信号（使用RPM/瓦里安），并使用EMD将其数据分解为固有模态函数（IMF）。然后将HHT应用于每个IMF以获得作为时间函数的瞬时频率。将频率信息的结果与快速傅里叶变换（FFT）和频率的手动计算进行比较。每个IMF与替代信号的相关性用于确定适当的IMF作为忠实的肿瘤运动替代物。
替代RPM信号平均分解为10±1个IMF。分解后的IMF显示出三类频率：（1）高频（1 - 10Hz），如类似噪声的信号；（2）中频（0.1 - 0.3Hz），这可能是真正的呼吸信号；（3）低频（0.003 - 0.09Hz），表现为基线漂移。边际频率是每个频率总幅度贡献的度量，与FFT的平均差异为-0.03±0.07，与手动计算的平均差异为-0.02±0.05。每个替代信号与一个IMF显示出高相关性（平均为0.747），与其余IMF显示出低相关性（平均为0.139）。仅具有高相关性的IMF在呼吸频率和幅度方面很好地代表了替代信号。
EMD和HHT用于在时域中分析非线性和非平稳替代信号的循环分量。由于EMD将信号分解为具有物理意义的模式，因此在去除类似噪声的信号后，可以确定忠实地代表肿瘤运动的IMF。对IMF物理意义的进一步研究是该研究的下一步。
