Replacement. Professor Mark Taylor ... Radcliffe et al, PhD Thesis, 2007. N=16. Influence ... Hypermesh (Booleans) -> Ansys ICEM (meshing)-> Marc MSC. (FE).
Moving Towards Population Based Computational Modelling of Total Joint Replacement Professor Mark Taylor
Total Joint Replacement Excellent survivorship at 10 years New designs regularly enter the market Increasingly difficult to assess whether design changes will improve performance
Sources of Variability The Patient
•Age/activity level •Bone quality/geometry •Soft tissue quality •Body weight
Surgery
•Experience •Personal preference •Alignment •Surgical approach
Femoral Head Resurfacing Initial early-mid term clinical results impressive However: High incidence of femoral neck fracture in first 6 months 5 fold increase in revision rate in small diameter heads as compared to large http://www.orthoassociates.com diameter heads1 1Shimmin
et al, JBJS(Br), 2010
FE analysis of the resurfaced femoral head: Modelling of an individual patient
Subject specific models 3x BW 1x BW
Subject specific models - Significant strain shielding within the head - Increase in strain on the superior aspect of the neck - Peak strain occurs around the inferior aspect of the neck
Comparison of a small vs. large femur
Small femur
Large femur
Typical FE analysis of the resurfaced femoral head Typically model the “average” patient Ideal implantation, single size Parametric studies on limited number of variables Attempt to extrapolate results to larger patient population Patient variability swamps differences?
Typical FE analysis of the resurfaced femoral head Typically model the “average” patient Ideal implantation, single size This will not predict small percentage of failures Parametric studies on limited Radical re-think of pre-clinical testing needed! number of variables Attempt to extrapolate results to larger patient population Patient variability swamps differences?
FE analysis of the resurfaced femoral head: Modelling of 10’s of patients
The brute force approach
xN
- Model multiple femurs from a range of patients - Examine mean, standard deviation, range…. - Perform statistical tests when comparing designs
Radcliffe et al, Clin. Biomech., 2007
The brute force approach 200
45
180
40
160
35
140
30
120
25
100 20
80
15
60 40
10
20
5
0
0 Hip 609
Hip 613
Hip 628
Hip 631
Hip 636
Hip 608
Hip 626
Hip 607
Hip 625
Hip 612
Hip 610
Hip 630
Hip Number Height (cm)
Weight (kg)
BMI
Weight: 95.312 kg (54 – 136) Height: 1.76 m (1.57 – 1.88) Age: 40.75 years (18 – 57) Gender: male dominated
Hip 614
Hip 635
Hip 627
Hip 634
BMI
Height (cm) / Weight (kg)
Patient Data
Influence of cementing the stem
N=16
Radcliffe et al, PhD Thesis, 2007
Influence of cementing the stem
N=16
Radcliffe et al, PhD Thesis, 2007
Influence of implant position
N=16
Radcliffe et al, PhD Thesis, 2007
The brute force approach
N=16
- Very labour intensive -Impractical to examine 100’s of femurs - Still difficult to compare differences across sizes Radcliffe et al, PhD Thesis, 2007
FE analysis of the resurfaced femoral head: Modelling of 100’s of patients
Principal Component Analysis Construction of a Statistical Model
Statistical Shape and Intensity Model (n=46) Mode 1 – Scaling of morphology and properties Mode 2 – Scaling and neck anteversion Model 3 – Neck anteversion and head/neck ratio
Bryan et al, Med. Eng. Phys., 2010
Generation of New Instances • Using governing PCA equation it is possible to generate new, realistic femur models from the variations captured by the model
Automated Implantation Automated Implantation – Run through Matlab Hypermesh (Booleans) -> Ansys ICEM (meshing)-> Marc MSC (FE)
Fully scripted from statistical model to FE results
Representative examples from N=400 Modulus
Modulus
Strain
Results (N=400)
Bryan et al, J. Biomech., 2012
Results (N=400)
Bryan et al, J. Biomech., 2012
Results - Comparison between head sizes
N=20
N=25 Bryan et al, J. Biomech., 2012 Small diameter heads show: - Increased strain shielding - Elevated strains at the superior femoral neck
Statistical Shape and Intensity Model • Developed methodology has significant potential for improving preclinical assessment • There are issues: • Statistical shape and intensity models only as good as the training set • Robust automation • Forces may need to link with musculoskeletal models • Verification/validation
Future directions……. Drive for ‘real time’ tools
Femoral neck fracture (KAIST, Korea)
Implant Positioning (Imperial College, UK)
Diaphyseal fracture reduction (Brainlab, Germany)
Rapid patient specific modelling………
Surrogate model FR = axb + cyd +……
100’s to 1000s of simulations
FE simulation
Surrogate model
Approx. 300 secs
Approx. 0.2 secs
Acknowledgements
Dr Rebecca Bryan Dr Ian Radcliffe Dr Mike Strickland Dr Francis Galloway Dr Martin Browne Dr Prasanth Nair
