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基于频散曲线和机器学习方法的层状生物软组织特性反演

INVERSION OF LAYERED BIOLOGICAL SOFT TISSUE PROPERTIES BASED ON METHOD OF DISPERSION CURVES AND MACHINE LEARNING

  • 摘要: 生物软组织的几何和力学性质是多种疾病的重要指标, 通过高精度反演得到生物软组织特性, 如组织厚度、弹性模量和密度等, 对疾病的诊断、治疗和康复都具有重要的医疗指导意义, 超声波检测中包含的参数信息可以高效并准确地解决参数反演问题. 文章将超声导波的频散曲线和机器学习相结合, 建立了基于生物软组织Lamb波频散曲线的BP神经网络反演方法, 分析了频散曲线的模态、波段、训练数据个数、波数采样点个数和数据噪声对反演精度的影响. 研究结果表明, 提出的反演方法能够准确地得到生物软组织的单参数特性, 并且具有很好的普适性, 可用于生物软组织的不同特性参数的反演. 其中频散曲线数据通过求解频散方程获取. 引入确定系数R2对反演结果进行评估, 通过对比不同模态与频段下的反演结果, 证明根据修正Morris法求得的灵敏度指标来选取频散曲线样本的模态与波段是一种有效手段. 而且采用混合模态可进一步提升反演精度. 训练数据个数和波数采样点个数超过相应的阈值后, 反演精度基本不变 , 根据阈值可同时保证优异的反演精度和反演效率. 通过在样本中加入随机噪声进行鲁棒性测试, 当训练样本和测试样本的取值范围和噪声水平相近时, 本文反演方法表现出一定的鲁棒性.

     

    Abstract: Geometric and mechanical properties of biological soft tissue are important indicators of many diseases. The properties of biological soft tissue acquired by high-precision inversion, such as tissue thickness, elastic modulus, density and so on, can provide significant medical reference to the diagnosis, treatment and recovery of diseases. The parameter information in ultrasonic detection can efficiently and accurately solve the parameter inversion problem. By combining dispersion curves of ultrasonic guided waves with machine learning, a back propagation neural network inversion method based on the Lamb wave dispersion curves of biological soft tissue was established in this paper. The effects of the dispersion mode, waveband, training data number, wavenumber sampling number and data noise on the inversion accuracy were analyzed. The results show that the inversion method combined dispersion curves and machine learning can accurately derive the single parameter properties of biological soft tissue. Moreover, this method has good universality and can be applied to the inversion of various characteristic parameters. The data of dispersion curves are obtained by solving the dispersion equation of biological soft tissue. The determination coefficientR2 is introduced to evaluate the inversion results. Comparison among the inversion results using different modes and wavebands of dispersion curves proves that the dispersion mode and waveband of dispersion curves can be effectively and properly selected according to the sensitivity index obtained by the modified Morris method. Adopting mixed dispersion modes can further improve the inversion accuracy. When training data number and wavenumber sampling number reach certain thresholds, the inversion accuracy is basically unchanged. According to thresholds, both excellent inversion accuracy and inversion efficiency can be guaranteed. Robustness test was conducted by adding random noise to the sample data. The proposed inversion method in this paper shows considerable robustness when the training and testing samples have similar parameter value ranges and noise levels.

     

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