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Population Based Implant Evaluations
Overview
The performance of total knee replacement implants is sensitive to intersubject and surgical variability. Subject-specific finite element (FE) analyses have been used to evaluate the joint mechanics of implant designs. This case study highlights the development of an FE platform to perform population-based evaluations of implant in activities of daily living and considering the impact of variability.
Characteristics:
- Geometry developed from magnetic resonance images
- Representations of bones, cartilage, ligaments and muscles for the lower limb segmented using ScanIP
- Statistical shape model constructed for a training set of segmented geometries
- FE-based musculoskeletal modelling performed using control system capabilities in Abaqus®
Thanks to
Center for Orthopaedic Biomechanics, University of Denver:
P.J. Laz • C.K. Fitzpatrick • P.J. Rullkoetter
Image Processing
Detailed representations of the structures of the knee, including femur, tibia, patella and associated cartilage and ligaments, were segmented from magnetic resonance (MR) images using ScanIP. This was performed for 40 subjects to serve as a training set for a statistical shape model. In addition, a complete model of the bones and muscles of the lower limb were developed from cryosection slice images of the visible human
Statistical Shape Model
Statistical shape modelling characterizes the common modes of variation in the training set geometries. The model is created by establishing correspondence between nodes and then performing a principal component analysis. The shape model results in a series of modes characterizing the changes in geometry and alignment. As the statistical shape model is based on an FE mesh, the platform integrates seamlessly into analysis and can generate new instances to facilitate population-based investigations.
Simulation
Finite element investigations have explored relationships between shape and function in the natural knee and evaluated the impact of intersubject and alignment variability in the implanted knee. These probabilistic evaluations predict the bounds of performance in joint mechanics and identify the most important input parameters. Current work is focused on the development of forward-dynamic muscle-driven models to perform implant evaluations under activities of daily living. Using a control system, external loads are applied to the hip, ankle and muscle actuators to reproduce in vivo kinematics measured from gait and fluoroscopy data.