腓肠肌羽状角的超声自动测量

摘要

目的 本文提出腓肠肌羽状角的一种基于超声射频信号的互相关自动测量方法.方法 本文采集正常人体腓肠肌与仿真腓肠肌体模的超声射频信号,重建超声图像,确定肌束的搜索起始点,采用互相关算法自动跟踪肌束膜和肌腱膜,绘出肌束线和肌腱膜线,计算两线之夹角,即为腓肠肌羽状角,并检验自动与手动两种测量方法的可重复性及一致性.结果 利用互相关自动测量方法可准确测量出体膜中仿真肌束羽状角,其测量值的均值与理论值之间的差异小于1°.利用互相关自动测量方法与手动测量方法获得的正常人体腓肠肌羽状角分别为20.48°±0.47°与21.49°±1.79°,两种方法的变异系数CV值均小于3%,均方根误差均小于1°.Bland-Altman差值法分析结果表明两种方法的一致性良好.结论 本文所提出的腓肠肌羽状角自动测量方法是基于超声射频信号的互相关算法,其测量结果与手动测量结果基本一致,在较严重的噪声情况下仍可跟踪腓肠肌的肌束走向,自动测量腓肠肌的羽状角.%Objective We propose a cross-correlation method for automatic extraction of the pennation angle (PA) of the gastrocnemius (GM) muscle from ultrasound radiofrequency (RF) signals. Methods The ultrasound RF signals of the GM muscles in tension condition from normal subjects and the simulated ultrasound signals were collected. After the starting point of tracking, a fascicle was selected in the reconstructed GM ultrasound image from the RF signals, and the fascicle and deep aponeurosis could be automatically tracked using the cross-correlation algorithm. The lines of the fascicle and deep aponeurosis were then drawn and the PA was calculated. The reproducibility of the proposed method and its consistency with the manual measurement method were tested. Results The angles of the simulated fascicles were precisely extracted automatically. The difference between the experimental measurement and the theoretical values was less than 1° . The PA measured automatically and manually was 20.48°±0.47° and 21.49°±1.79°, respectively. The coefficient of variation (CV) of the two methods was less than 3% and the root-mean square error (RMSE) was less than 1° . Bland-Altman plot showed a good agreement between the proposed automatic method and the manual method. Conclusion The proposed cross-correlation automatic measurement method can detect the orientation of the fascicle and deep aponeurosis and measure the PA based on ultrasound RF signals with serious speckle noise.