Computational framework for population-based evaluation of TKR-implanted patellofemoral joint mechanics

Abstract

Differences in patient anatomy are known to influence joint mechanics. Accordingly, intersubject anatomical variation is an important consideration when assessing the design of joint replacement implants. The objective of this study was to develop a computational workflow to perform population-based evaluations of total knee replacement implant mechanics considering variation in patient anatomy and to assess the potential for an efficient sampling strategy to support design phase screening analyses. The approach generated virtual subject anatomies using a statistical shape model of the knee and performed virtual implantation to size and align the implants. A finite-element analysis simulated a deep knee bend activity and predicted patellofemoral (PF) mechanics. The study predicted bounds of performance for kinematics and contact mechanics and investigated relationships between patient factors and outputs. For example, the patella was less flexed throughout the deep knee bend activity for patients with an alta patellar alignment. The results also showed the PF range of motions in AP and ML were generally larger with increasing femoral component size. Comparison of the 10-90% bounds between sampling strategies agreed reasonably, suggesting that Latin Hypercube sampling can be used for initial screening evaluations and followed up by more intensive Monte Carlo simulation for refined designs. The platform demonstrated a functional workflow to consider variation in joint anatomy to support robust implant design.

Keywords: Monte Carlo simulation; Patellofemoral joint mechanics; Population-based evaluation; Statistical shape modeling; Total knee replacement.

Figure 1.

Computational workflow for population-based evaluation of TKR joint mechanics.

Figure 2.

Automatic implantation with SSM-generated virtual subjects (top) and implanted instances (middle). Patellar button implantation algorithm (bottom left) and PF implant occurrences (bottom right).

Figure 3.

Cumulative distribution function comparison of SSM training set and sampled analysis groups (Monte Carlo, Latin Hypercube) to prior population study on 1000 subjects with varying ethnicity (Mahfouz et al. 2012).

Figure 4.

Comparison of implant sizes from Monte Carlo sampling (100 instances) using the virtual implantation algorithm to clinical data on 100 consecutive TKR patients.

Figure 5.

Comparison of PF kinematics for flexion-extension (FE), internal-external (IE) and medial-lateral (ML) degrees of freedom between model-predicted Monte Carlo (gray), model-predicted Latin Hypercube (red), and in-vitro experimental data (black).

Figure 6.

Joint motion with flexion for a representative instance (top). Influence of implant size on PF ML and AP range of motion (middle), and influence of patellar alta-baja on PF FE and ML (bottom). Error bars represent one standard deviation.