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Particle filter for implanted knee kinematic analysis using dynamic radiograph video

机译:粒子过滤器通过动态射线照相视频进行膝关节运动学分析

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Implanted knee kinematic analysis plays one of important role in clinical and research fields of total knee arthroplasty. Although there are some studies to analyze X-ray images for estimating 3-D knee kinematics, most of them cannot analyze dynamic video because they strongly depend on manual interaction of giving initial pose/position. This paper utilizes particle filter for analyzing dynamic radiograph video of implanted knee. By using particle filter, the proposed method does not require not only user interaction but also computational iteration of parameter optimization. As the result, we shorten the computation time and improved the estimation accuracy. The estimation error was lower than 0.7 mm for rotation, and 0.5 mm for translation including out-plane, and the computation time was 1.27 sec per frame using a cluster computer.
机译:植入式膝关节运动学分析在全膝关节置换术的临床和研究领域中起着重要作用之一。尽管有一些研究分析X射线图像以估计3-D膝关节运动学,但大多数研究不能分析动态视频,因为它们强烈依赖于给出初始姿势/位置的手动交互。本文利用粒子滤波技术分析了植入膝关节的动态X光片。通过使用粒子滤波器,该方法不仅不需要用户交互,还不需要参数优化的计算迭代。结果,我们缩短了计算时间并提高了估计精度。旋转的估计误差低于0.7毫米,平移(包括外平面)的估计误差低于0.5毫米,使用群集计算机的计算时间为每帧1.27秒。

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