Center for Biomedical Engineering, Brown University;
Department of Orthopedics, The Warren Alpert Medical School of Brown University and Rhode Island Hospital.
J Vis Exp. 2021 Feb 4(168). doi: 10.3791/62102.
Accurate measurement of skeletal kinematics in vivo is essential for understanding normal joint function, the influence of pathology, disease progression, and the effects of treatments. Measurement systems that use skin surface markers to infer skeletal motion have provided important insight into normal and pathological kinematics, however, accurate arthrokinematics cannot be attained using these systems, especially during dynamic activities. In the past two decades, biplanar videoradiography (BVR) systems have enabled many researchers to directly study the skeletal kinematics of the joints during activities of daily living. To implement BVR systems for the distal upper extremity, videoradiographs of the distal radius and the hand are acquired from two calibrated X-ray sources while a subject performs a designated task. Three-dimensional (3D) rigid-body positions are computed from the videoradiographs via a best-fit registrations of 3D model projections onto to each BVR view. The 3D models are density-based image volumes of the specific bone derived from independently acquired computed-tomography data. Utilizing graphics processor units and high-performance computing systems, this model-based tracking approach is shown to be fast and accurate in evaluating the wrist and distal radioulnar joint biomechanics. In this study, we first summarized the previous studies that have established the submillimeter and subdegree agreement of BVR with an in vitro optical motion capture system in evaluating the wrist and distal radioulnar joint kinematics. Furthermore, we used BVR to compute the center of rotation behavior of the wrist joint, to evaluate the articulation pattern of the components of the implant upon one another, and to assess the dynamic change of ulnar variance during pronosupination of the forearm. In the future, carpal bones may be captured in greater detail with the addition of flat panel X-ray detectors, more X-ray sources (i.e., multiplanar videoradiography), or advanced computer vision algorithms.
准确测量体内骨骼运动学对于理解正常关节功能、病理影响、疾病进展以及治疗效果至关重要。使用皮肤表面标记来推断骨骼运动的测量系统为正常和病理运动学提供了重要的见解，然而，这些系统无法准确获得关节运动学，尤其是在动态活动中。在过去的二十年中，双平面 X 射线摄影（BVR）系统使许多研究人员能够在日常生活活动中直接研究关节的骨骼运动学。为了实现远端上肢的 BVR 系统，需要从两个校准的 X 射线源获取桡骨远端和手部的 X 射线视频，同时让受试者执行指定的任务。通过将 3D 模型投影到每个 BVR 视图上的最佳拟合注册，从 X 射线视频中计算出 3D 刚体位置。3D 模型是从独立获取的计算机断层扫描数据中特定骨骼的基于密度的图像体积。利用图形处理单元和高性能计算系统，这种基于模型的跟踪方法在评估手腕和远端桡尺关节生物力学方面表现出快速和准确。在本研究中，我们首先总结了之前的研究，这些研究已经建立了 BVR 在评估手腕和远端桡尺关节运动学方面与体外光学运动捕捉系统的亚毫米和亚度的一致性。此外，我们使用 BVR 计算了手腕关节的旋转中心行为，评估了植入物组件之间的关节模式，并评估了前臂旋前和旋后过程中尺侧变异的动态变化。在未来，可以通过添加平板 X 射线探测器、更多的 X 射线源（即多平面 X 射线摄影）或先进的计算机视觉算法来更详细地捕获腕骨。
