Deep Learning Analysis of Ultrasonic Guided Waves for Cortical Bone Characterization

皮质骨 时域有限差分法 反问题 成像体模 超声波传感器 反向 均方误差 卷积神经网络 横截面 数学 材料科学 声学 数学分析 计算机科学 算法 物理 光学 几何学 人工智能 工程类 结构工程 统计 解剖 医学
作者
Yifang Li,Kailiang Xu,Ying Li,Feng Xu,Dean Ta,Weiqi Wang
出处
期刊:IEEE Transactions on Ultrasonics Ferroelectrics and Frequency Control [Institute of Electrical and Electronics Engineers]
卷期号:68 (4): 935-951 被引量:32
标识
DOI:10.1109/tuffc.2020.3025546
摘要

Ultrasonic guided waves (UGWs) propagating in the long cortical bone can be measured via the axial transmission method. The characterization of long cortical bone using UGW is a multiparameter inverse problem. The optimal solution of the inverse problem often includes a complex solving process. Deep neural networks (DNNs) are essentially powerful multiparameter predictors based on universal approximation theorem, which are suitable for solving parameter predictions in the inverse problem by constructing the mapping relationship between UGW and cortical bone material parameters. In this study, we investigate the feasibility of applying the multichannel crossed convolutional neural network (MCC-CNN) to simultaneously estimate cortical thickness and bulk velocities (longitudinal and transverse). Unlike the multiparameter estimation in most previous studies, the technique mentioned in this work avoids solving a multiparameter optimization problem directly. The finite-difference time-domain (FDTD) method is performed to obtain the simulated UGW array signals for training the MCC-CNN. The network that is exclusively trained on simulated data sets can predict cortical parameters from the experimental UGW data. The proposed method is confirmed by using FDTD simulation signals and experimental data obtained from four bone-mimicking plates and from ten ex vivo bovine cortical bones. The estimated root-mean-squared error (RMSE) in the simulated test data for the longitudinal bulk velocity ( VL ), transverse bulk velocity ( VT ), and cortical thickness (Th) is 97 m/s, 53 m/s, and 0.089 mm, respectively. The predicted RMSE in the bone-mimicking phantom experiments for VL|| , VT|| , and Th is 120 m/s, 80 m/s, and 0.14 mm, respectively. The experimental dispersion trajectories are matched with the theoretical dispersion curves calculated by the predicted parameters in ex vivo bovine cortical bone experiments. Our proposed method demonstrates a feasible approach for the accurate evaluation of long cortical bones based on UGW.
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