Farron Joe, Varghese Tomy, Thelen Darryl G
Department of Mechanical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
IEEE Trans Ultrason Ferroelectr Freq Control. 2009 Jan;56(1):27-35. doi: 10.1109/TUFFC.2009.1002.
A 2-D strain estimation algorithm was used to estimate tendon strain from ultrasound data collected during muscle twitch contractions. We first used speckle tracking techniques to estimate frame-to-frame displacements of all pixels within a rectangular region of interest (ROI) positioned over a tendon. A weighted, least-squares approach was then solved for the displacements of the ROI endpoints that best fit the pixel displacements. We summed endpoint displacements across successive frames to determine the cumulative endpoint motion, which was then used to estimate the cumulative strain along the tendinous fibers. The algorithm was applied to ultrasound radiofrequency data, acquired at 74 frames per second over the tibialis anterior (TA) musculotendon junction (MTJ). The TA muscle was electrically stimulated with the subject holding voluntary preloads of 0%, 10%, 20%, 30%, 40%, and 50% of a maximum voluntary contraction (MVC). Peak tendon strains computed using elastography (0.06 to 0.80%) were slightly larger and occurred earlier (50-90 ms after stimulus) than calculations based on visual analysis of B-mode images. This difference likely reflected the more localized nature of the elastographic strain values. Estimates of the tangential elastic modulus (192 +/- 58 MPa) were consistent with literature values obtained using more direct approaches. It is concluded that automated elastographic approaches for computing in vivo tendon strains could provide new insights into musculotendon dynamics and function.
采用二维应变估计算法,根据肌肉抽搐收缩期间收集的超声数据估算肌腱应变。我们首先使用散斑跟踪技术来估计位于肌腱上方的矩形感兴趣区域(ROI)内所有像素的逐帧位移。然后采用加权最小二乘法求解最符合像素位移的ROI端点位移。我们对连续帧中的端点位移进行求和,以确定累积端点运动,进而用于估计沿肌腱纤维的累积应变。该算法应用于在胫前肌(TA)肌腱结合处(MTJ)以每秒74帧采集的超声射频数据。在受试者保持最大自主收缩(MVC)的0%、10%、20%、30%、40%和50%的自主预负荷时,对TA肌肉进行电刺激。使用弹性成像计算的峰值肌腱应变(0.06%至0.80%)略大,且比基于B模式图像视觉分析的计算结果出现得更早(刺激后50 - 90毫秒)。这种差异可能反映了弹性成像应变值更具局部性的特点。切向弹性模量的估计值(192±58兆帕)与使用更直接方法获得的文献值一致。结论是,用于计算体内肌腱应变的自动弹性成像方法可为肌腱动力学和功能提供新的见解。