Aim of this study was the development of a dynamic FE-framework to identify worst-case size combinations and kinematics in a virtual wear simulator setup covering five daily activities and high, dynamic loads. Two cruciate sacrificing knee designs (D1 & D2) were tested physically on a wear-testing machine prior the model development using a high demanding, daily activity protocol (HDA) [1]. A simplified FE-setup was generated, reduced to the 3D geometries of the assembly whereas the representation of the mechanical wear simulator conditions and the load transmission was achieved by joint elements. Inertial and other time-related effects of the physical situation were compensated by a system of spring- and damper elements. Using a time-series signal optimization approach on the anterior-posterior translation and the internal-external rotation results for each activity, 38 variable parameters were varied in between pre-defined limits in a semiautomatic workflow. For each design, two consecutive cycles of a single activity were analysed and the results of the second cycle were used for the optimization. Based on the determined values, a single set of averaged parameter settings was identified that covers all activity cycles sufficiently. A total of 1010 dynamic analyses were carried out in order to find a sharable set of parameter values. In this study, an efficient simulation workflow for design evaluation was developed. Therefore, a HDA wear-testing machine was simplified to boundary conditions and stabilizing elements, using a single set of parameters for all activities. The calculated kinematics were in a comparable range to the machine output. Further applications of the method were found in systematic analyses of entire implant systems to achieve consistent kinematics over the size compatibility range in the design process of new implant systems.
Acetabular bone defects are still challenging to quantify. Numerous classification schemes have been proposed to categorize the diverse kinds of defects. However, these classification schemes are mainly descriptive and hence it remains difficult to apply them in pre-clinical testing, implant development and pre-operative planning. By reconstructing the native situation of a defect pelvis using a Statistical Shape Model (SSM), a more quantitative analysis of the bone defects could be performed. The aim of this study is to develop such a SSM and to validate its accuracy using relevant clinical scenarios and parameters. An SSM was built on the basis of segmented 66 CT dataset of the pelvis showing no orthopedic pathology. By adjusting the SSM's so called The validity of the SSM was tested by a Leave-one-out study, whereby one pelvis at a time was removed from the 66 pelvises and was reconstructed using a SSM of the remaining 65 pelvises. The reconstruction accuracy was assessed by comparing each original pelvis with its reconstruction based on the root-mean-square (RMS) surface error and five clinical parameters (center of rotation, acetabulum diameter, inclination, anteversion, and volume). The influence of six different numbers of shape variation modes (reflecting the degrees of freedom of the SSM) and four different mask sizes (reflecting different clinical scenarios) was analyzed.Introduction
Methods
In TKA the generation of polyethylene wear debris is mainly affected by the factors design of the articulating bearing, contact stresses, kinematics, implant material and surface finish [McEwen et al. 2005]. The objective of our study was to evaluate the in vitro wear behaviour of fixed bearing knee designs in comprehension to the contact mechanics and resultant kinematics for different degrees of congruency.
For gravimetric wear assessment the protocol described in ISO 14243-2 has been used, followed by a kinematic analysis of the single test stations. The articulating contact and subsurface stresses have been investigated in a finite element analysis.
The wear rates between the knee design configurations differ substantially and statistically analysis demonstrates a significant difference (p<
0.01) between the test groups in correlation with congruency.