Department of Electronic Engineering, Fudan University, Shanghai, PR China.
IEEE Trans Ultrason Ferroelectr Freq Control. 2010 Nov;57(11):2480-90. doi: 10.1109/TUFFC.2010.1714.
Quantitative ultrasound has great potential for assessing human bone quality. Considered as an elastic waveguide, long bone supports propagation of several guided modes, most of which carry useful information, individually, on different aspects of long bone properties. Therefore, precise knowledge of the behavior of each mode, such as velocity, attenuation, and amplitude, is important for bone quality assessment. However, because of the complicated characteristics of the guided waves, including dispersion and mode conversion, the measured signal often contains multiple wave modes, which yields the problem of mode separation. In this paper, some novel signal processing approaches were introduced to solve this problem. First, a crazy-climber algorithm was used to separate time-frequency ridges of individual modes from time-frequency representations (TFR) of multimodal signals. Next, corresponding time domain signals representing individual modes were reconstructed from the TFR ridges. It was found that the separated TFR ridges were in agreement with the theoretical dispersion, and the reconstructed signals were highly representative of the individual guided modes as well. The validations of this study were analyzed by simulated multimodal signals, with or without noise, and by in vitro experiments. Results of this study suggest that the ridge detection and individual reconstruction method are suitable for separating individual modes from multimodal signals. Such a method can improve the analysis of skeletal guided wave signals by providing accurate assessment of mode-specific ultrasonic parameters, such as group velocity, and indicate different bone quality properties.
超声定量具有评估人体骨骼质量的巨大潜力。作为弹性波导，长骨支持多种导波模式的传播，其中大多数模式分别携带关于长骨特性不同方面的有用信息。因此，精确了解每种模式的行为（如速度、衰减和幅度）对于骨骼质量评估很重要。然而，由于导波的复杂特性，包括色散和模式转换，测量信号通常包含多个波模式，从而产生了模式分离的问题。在本文中，介绍了一些新的信号处理方法来解决这个问题。首先，使用疯狂攀爬算法从多模态信号的时频表示（TFR）中分离单个模式的时频脊。接下来，从 TFR 脊中重建代表各个模式的相应时域信号。结果发现，分离的 TFR 脊与理论色散一致，重建信号也高度代表各个导波模式。通过模拟的多模态信号（带或不带噪声）和体外实验对本研究的验证进行了分析。本研究的结果表明，脊检测和个体重建方法适用于从多模态信号中分离各个模式。这种方法可以通过提供对特定于模式的超声参数（如群速度）的准确评估，来改善骨骼导波信号的分析，并指示不同的骨骼质量特性。
