Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts
Prediction of in vivo joint mechanics of an artificial knee implant using rigid multi-body dynamics with elastic contacts
复制标题
使用具有弹性接触的刚性多体动力学预测人工膝关节植入物的体内关节力学
DOI：
10.1177/0954411914537476
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发表时间：
2014-06-01
期刊：
影响因子：
1.8
通讯作者：
Jin, Zhongmin
中科院分区：
文献类型：
--
作者：
Chen, Zhenxian;Zhang, Xuan;Jin, Zhongmin
下肢肌肉骨骼计算模型在同时预测关节力和肌肉激活方面发挥着重要作用，对于研究植入物的功能结果是有价值的。然而，目前的全膝关节置换术的计算肌肉骨骼模型很少考虑植入物的支撑面几何形状。因此，这些模型缺乏关于接触载荷和关节运动的详细信息，而接触载荷和关节运动是评估临床性能的重要因素。该研究基于一种新的力相关运动学方法，扩展了基于腿部肌肉骨骼模型的刚性多体动力学仿真，加入了人工膝关节，并描述了步态中关节接触的力学特性。研制的肌肉骨骼全膝关节置换术模型融合了刚性骨骼多体动力学和胫股关节和髌股关节的柔性接触力学。将预测的接触力和肌肉激活与从单个患者获得的体内测量结果进行比较，其中内侧接触力(均方根误差=215N，ρ=0.96N)和侧接触力(均方根误差=179N，ρ=0.75)具有良好的一致性。此外，所开发的模型还预测了胫股关节在所有自由度下的运动。这一新的模型为开发一种现实的动态肌肉骨骼全膝关节置换模型提供了重要的一步，该模型可以同时预测在体膝关节的运动和载荷。这可以为未来膝关节假体设计、手术过程和术后康复的临床前评估提供更好的机会来建立一个健壮的虚拟建模平台。
Lower extremity musculoskeletal computational models play an important role in predicting joint forces and muscle activation simultaneously and are valuable for investigating functional outcomes of the implants. However, current computational musculoskeletal models of total knee replacement rarely consider the bearing surface geometry of the implant. Therefore, these models lack detailed information about the contact loading and joint motion which are important factors for evaluating clinical performances. This study extended a rigid multi-body dynamics simulation of a lower extremity musculoskeletal model to incorporate an artificial knee joint, based upon a novel force-dependent kinematics method, and to characterize the in vivo joint contact mechanics during gait. The developed musculoskeletal total knee replacement model integrated the rigid skeleton multi-body dynamics and the flexible contact mechanics of the tibiofemoral and patellofemoral joints. The predicted contact forces and muscle activations are compared against those in vivo measurements obtained from a single patient with good agreements for the medial contact force (root-mean-square error = 215 N, ρ = 0.96) and lateral contact force (root-mean-square error = 179 N, ρ = 0.75). Moreover, the developed model also predicted the motion of the tibiofemoral joint in all degrees of freedom. This new model provides an important step toward the development of a realistic dynamic musculoskeletal total knee replacement model to predict in vivo knee joint motion and loading simultaneously. This could offer a better opportunity to establish a robust virtual modeling platform for future pre-clinical assessment of knee prosthesis designs, surgical procedures and post-operation rehabilitation.