Ferroudji Karim, Benoudjit Nabil, Bouakaz Ayache
Laboratoire d'Automatique Avancée et d'Analyse des Systèmes (LAAAS), Université de Batna-2, Fesdis, Algeria.
UMR Inserm U930-Imagerie et cerveau, Université François Rabelais de Tours, Tours, France.
Australas Phys Eng Sci Med. 2017 Mar;40(1):85-99. doi: 10.1007/s13246-016-0512-4. Epub 2017 Jan 9.
Embolic phenomena, whether air or particulate emboli, can induce immediate damages like heart attack or ischemic stroke. Embolus composition (gaseous or particulate matter) is vital in predicting clinically significant complications. Embolus detection using Doppler methods have shown their limits to differentiate solid and gaseous embolus. Radio-frequency (RF) ultrasound signals backscattered by the emboli contain additional information on the embolus in comparison to the traditionally used Doppler signals. Gaseous bubbles show a nonlinear behavior under specific conditions of the ultrasound excitation wave, this nonlinear behavior is exploited to differentiate solid from gaseous microemboli. In order to verify the usefulness of RF ultrasound signal processing in the detection and classification of microemboli, an in vitro set-up is developed. Sonovue micro bubbles are exploited to mimic the acoustic behavior of gaseous emboli. They are injected at two different concentrations (0.025 and 0.05 µl/ml) in a nonrecirculating flow phantom containing a tube of 0.8 mm in diameter. The tissue mimicking material surrounding the tube is chosen to imitate the acoustic behavior of solid emboli. Both gaseous and solid emboli are imaged using an Anthares ultrasound scanner with a probe emitting at a transmit frequency of 1.82 MHz and at two mechanical indices (MI) 0.2 and 0.6. We propose in this experimental study to exploit discrete wavelet transform and a dimensionality reduction algorithm based on differential evolution technique in the analysis and the characterization of the backscattered RF ultrasound signals from the emboli. Several features are evaluated from the detail coefficients. It should be noted that the features used in this study are the same used in the paper by Aydin et al. These all features are used as inputs to the classification models without using feature selection method. Then we perform feature selection using differential evolution algorithm with support vector machines classifier. The experimental results show clearly that our proposed method achieves better average classification rates compared to the results obtained in a previous study using also the same backscatter RF signals.
栓塞现象，无论是空气栓塞还是颗粒栓塞，都可能引发如心脏病发作或缺血性中风等即时损伤。栓子成分（气体或颗粒物质）对于预测具有临床意义的并发症至关重要。使用多普勒方法检测栓子已显示出其在区分固体和气体栓子方面的局限性。与传统使用的多普勒信号相比，由栓子反向散射的射频（RF）超声信号包含有关栓子的额外信息。在超声激发波的特定条件下，气泡表现出非线性行为，利用这种非线性行为来区分固体和气体微栓子。为了验证RF超声信号处理在微栓子检测和分类中的有用性，开发了一种体外装置。利用声诺维微泡来模拟气体栓子的声学行为。将它们以两种不同浓度（0.025和0.05微升/毫升）注入到一个包含直径为0.8毫米的管子的非循环流动模型中。选择围绕管子的组织模拟材料来模仿固体栓子的声学行为。使用安塔雷斯超声扫描仪对气体和固体栓子进行成像，该扫描仪配备一个发射频率为1.82兆赫兹、两个机械指数（MI）分别为0.2和0.6的探头。在本实验研究中，我们提议利用离散小波变换和基于差分进化技术的降维算法来分析和表征来自栓子的反向散射RF超声信号。从细节系数中评估了几个特征。应当指出的是，本研究中使用的特征与艾登等人论文中使用的特征相同。所有这些特征都用作分类模型的输入，而不使用特征选择方法。然后我们使用带有支持向量机分类器的差分进化算法进行特征选择。实验结果清楚地表明，与之前一项同样使用相同反向散射RF信号的研究结果相比，我们提出的方法实现了更好的平均分类率。
