Pankaj PankajWe are consistently ranked one of the top 50 universities in the world* * QS, THES –...
Transcript of Pankaj PankajWe are consistently ranked one of the top 50 universities in the world* * QS, THES –...
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In silico modelling of the mechanical
behaviour of bone and bone-implant systems
Pankaj Pankaj School of Engineering,
Institute for Bioengineering
The University of Edinburgh
Scotland, UK
Indian Institute of Science, Bangalore – 18 February 2016
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P
Crisfield, MA (1991). Nonlinear Finite Element Analysis of Solids and
Structures, Wiley.
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We are consistently ranked one of the top 50 universities in the world* * QS, THES – Thomson Reuters, and Shanghai Jiao Tong Rankings 2015
100% of our departments are conducting world leading research * • 2014 UK-wide Research Excellence Framework
Founded in 1583,the sixth-oldest university in the English-speaking world
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Influencing the world
since 1583
19 Nobel Prize winners and
15 Olympic medals
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Edinburgh Global
Relationships ‘Pop up campuses’ / annual visits India 2015
- 40 academics
India 2016
- Activities in Pune, Chennai, Mumbai, Delhi, Bengaluru, Vellore
- Participation in MII - Interaction with industry - Interaction with government - Student participation
Strategy
- Partnerships - Attracting best talent - Student mobility
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Motivation
Ageing population – fractures and mortality, implant suitability
Bone structure/behaviour of interest to pharmaceutical industry
Implants need to last longer
Patients expect to remain active
Potential to replace expensive clinical trials
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Modelling – Information required
Geometry: subdivided into elements – 1/2/3D
Boundary conditions and interactions
Material properties: linear/nonlinear, isotropic/anisotropic, time dependent/ independent
Loads: static/dynamic
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Boundary conditions
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Boundary conditions – stresses in the pelvis
Constrained at the sacro-iliac joints
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Boundary conditions – stresses in the pelvis
Muscles
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Boundary conditions – stresses in the pelvis
Ligaments
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Boundary conditions – in vivo?
Phillips, Pankaj, Howie,
Usmani, Simpson — J Med
Eng & Phy, 2007
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von Mises stress in the trabecular bone for the constrained BC model
Boundary conditions - constrained
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von Mises stress in the trabecular bone for the ML BC model
Boundary conditions – muscles and ligaments
Phillips, Pankaj, Howie, Usmani, Simpson — J Med Eng & Phy, 2007
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Bone microstructure
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Microarchitecture of the femoral head
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Morphometrical Parameters
BV/TV
Trabecular thickness (Tr. Th.)
Structural model thickness (SMI)
Degree of anisotropy (DA)
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Results
Radially
orientated
trabeculae in
epiphysis
Strong column
of trabeculae
from calcar to
physeal scar
Physeal
transition
Low density
in
inferomedial
head
Thin cortex
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Hip Fracture Fixation
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Femoral Scans
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3D: BV:TV (Density)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0
20
40
60
80
Cube Position
BV
/TV
(%
)
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Trabecular Thickness (Tb. Th.)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18
0.000
0.050
0.100
0.150
0.200
0.250
0.300
0.350
0.400
0.450
0.500
Cube Position
Tra
be
cu
lar T
hic
kn
es
s (m
m)
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Material properties: Elasticity
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Material properties
Variations – inhomogeneity
Linear elastic – isotropic or transtropic or
orthotropic or anisotropic
Time dependent – e.g. viscoelasticity
Inelasticity – e.g. plasticity
Etc
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Cortical bone data
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Periosteal
Endosteal
Female
20-year-old
Female
61-year-old
Female
84-year-old
Cortical bone
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μCT scans of cortices from the anterior
femoral midshaft, (Melbourne Femur
Collection)
27 females, 50 males
7μm isotropic scanning resolution
Scanning and micro-architecture analysis – Cooper et al. 2007
This study – variation of mechanical
properties with micro-architecture
Cortical bone – elastic properties with patient-specific parameters
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Select sub-samples
Cortical bone – elastic properties with patient-specific parameters
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Label solid/void
Cortical bone – elastic properties with patient-specific parameters
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Generate triangular surface. Approx.
20,000 triangles per surface
Cortical bone – elastic properties with patient-specific parameters
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Periosteal
Endosteal
Female
20-year-old
Female
61-year-old
Female
84-year-old
Cortical bone – elastic properties with patient-specific parameters
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Generate mesh
Approx 250k elements
Cortical bone – elastic properties with patient-specific parameters
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Constructed 3D image and mesh
Cortical bone – elastic properties with patient-specific parameters
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Six numerical strain states computationally
applied
Full elasticity tensor evaluated
Tensor reoriented to best fit orthotropy
assumption
Cortical bone – elastic properties with patient-specific parameters
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Q1: Is cortical bone in femoral midshaft
orthotropic?
A1: Yes
Periosteal: 0.16% median, 3.86% max
Endosteal: 2.16% median, 11.48% max
ORT
ORTError
max0
Cortical bone – elastic properties with patient-specific parameters
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Q2: Which indices of micro-architecture
correlate best with elastic constants?
A2: Ca.V/TV
We also considered Ca.S/TV, Ca.Dm/TV,
DA
Cortical bone – elastic properties with patient-specific parameters
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Variation of Young’s moduli vs Porosity (CaV/TV)
Well correlated
EL>EC>ER
EC and ER divergent with porosity
Results – E
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Results – νCL
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Results – periosteal-endosteal variation
E decreases with Ca.V/TV
Reduction prnounced at endosteal aspect
Found to be true for all elastic constants
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It providess all orthotropic constants
Ca.V/TV is best (and only required) predictor
All constants reduce with increasing porosity
Conclusions
Donaldson et al., 2011, IMechE, H
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μCT scans of trabecular bone from the
femoral head (Tissue Bank of the Scottish
National Blood Transfusion service)
10 female, 2 male (age 71.3 ± 7.6 years)
6 OA and 6 OP
30μm isotropic scanning resolution
Trabecular bone – elastic properties
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5mm cubic regions extracted from 10 locations for each head
Methodology – μCT scan data
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10 scans for each of head = 120 scans
Methodology - μCT scan data
Medial
plane
Central
plane
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Constructed 3D image and mesh
Methodology
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Results – regression analysis
Q2: Which indices of micro-architecture
correlate best with elastic constants?
A2: Porosity
We also considered Ca.S/TV, Ca.Dm/TV,
DA
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Variation of E_max/E_min vs location
Large range on both planes :
3.52 on central plane
2.75 on medial plane
Mean = 1.87
Wider range at medial plane
Mean OP = 1.95
Mean OA = 1.80
Results – Anisotropy
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Trabecular bone and snow
(a)
(b)
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Trabecular bone and snow
0.0
0.2
0.4
0.6
0.8
0.2 0.3 0.4 0.5
Nor
mal
ized
You
ng's
mod
ului
(E i
)
Normalized eigen values (MILi)
Snow
Boner2 = 0.86
r2 = 0.98
Srivastava et al.,
Cold Region Sc., In
press
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Material properties: Time-dependent
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Motivation
Trabecular bone exhibits time-dependent
behaviour
Time dependent nature needs to be explored to predict
Orthopaedic implant loosening
The response to traumatic events -
sudden dynamic loads
Age related non-traumatic fractures
50
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How to measure time-dependent behaviour – viscoelasticity?
Creep/Relaxation/Dynamic
Need inter-conversion methods
Creep may contain irrecoverable strains
Need loading and unloading experiments
51
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Creep-recovery experiments
52
Bovine femoral head Samples cored out
μCT scanning (Skyscanner 1172)
Machining cores
Creep-recovery experiments, Zwick
Sample with end-caps,
hydrated
Creep-recovery response
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Creep-recovery experiments…
53
20 cylindrical bovine samples (5 from trochanter, 15 from femoral head) with marrow
Force for 200 sec; recovery for 600 sec for each cycle Stresses corresponding to: 0.25, 0.4%, 0.6%, 0.85, 1%, 1.5%
strain
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Creep-recovery curves
Creep rate is higher with lower BV/TV Creep behaviour changes with stress levels
Irrecoverable strains exist at the end of each at cycles Creep part of the curve may contain plastic and residual
strains
54
BV/TV=15.36% BV/TV=35.23%
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Results: Dense sample, BVTV=35%
55
Linear VE models do not predict well the VE response even at very lower strains
Nonlinear VE models predict well
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Results
56
• Total strain is composed of viscoelastic and viscoplastic strains
• Coupled viscoelastic-viscoplastic model predicted strains accurately
Parameter Value
𝑁 3.00E+00
𝛼 3.50E-01
𝛽 3.00E-01
𝑘0 4.77E+00
𝑘1 7.99E+00
𝑘2 8.48E+02
𝜎0 6.62E+00
𝜂 2.85E-03
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Conclusions
Time-dependent response varies nonlinearly with stress levels and comprises recoverable (viscoelastic) and irrecoverable (viscoplastic) strains
Nonlinear VE-VP model predicts the behaviour well at apparent level
57
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Material properties: Strain-based plasticity
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Material properties – post-elastic modelling
Stress based criteria require too many parameters
Bone yield anisotropic in stress space isotropic in strain space
Experimental evidence to suggest that yielding of bone is based on strain rather than stress
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Strain-based criterion concept
Material properties – post-elastic modelling
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Strain-based criterion concept
Material properties – post-elastic modelling
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Strain-based criterion concept
Material properties – post-elastic modelling
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Material properties – post-elastic modelling
Pankaj and Donaldson, 2013, IJNMBE
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Material properties: Plasticity – nonlinear homogenisation
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Motivation
• Use FE to evaluate yield behaviour of trabecular
bone – nonlinear homogenisation
• Assess how properties translate from microscale to
macroscale when using different constitutive laws
for the bone tissue
• To relate apparent level yield surfaces to micro-
architectural indices
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Methods
• Two sample with very different
morphologies, scanned at 17 µm on a
µCT system
• Binary segmentation using Gomez et al.
approach*
• Coordinate axes of the stack of images
were aligned to MIL axes
• Scans transformed to voxelised FE
meshes through an in-house developed
Message Passing Interface (MPI) script
• The dense and porous models have 29M
and 13M degrees of freedom,
respectively.
• 5 mm cubes used
• Used the approach with 20 samples
FE meshes. Left – 26.9% BV/TV. Right – 12.3% BV/TV.
Right – Reconstructed scan. Left – Binarised image
* Gomez et al. A comparative study of automatic thresholding approaches for 3D x=ray tomography of trabecular bone. Med Phys (2013).
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Load cases
• Elastic homogenisation requires 6 uniaxial loading cases
• Nonlinear homogenisation requires “infinite” load cases
• 171 load cases applied to each sample with linear boundary displacements.
Same ratios
o 3 uniaxial normal tension
o 3 uniaxial normal compression
o 12 biaxial normal
o 8 triaxial normal
o 3 uniaxial shear
o 3 biaxial shear
o 1 triaxial shear
o 18 biaxial normal-shear
Different ratios
o 24 biaxial normal
o 48 triaxial normal
o 6 biaxial shear
o 6 triaxial shear
o 36 biaxial normal-shear
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Behaviour of different samples
Florencio et al., submitted
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Behaviour of different samples
Florencio et al., JMBBM, submitted
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Application - Ilizarov fixation
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2.1% porosity
C.Area = 319.2mm2
C.Th = 5.10mm
12.6% porosity
C.Area = 319.2mm2
C.Th = 5.10mm
24.0% porosity
C.Area = 265.3mm2
C.Th = 3.64mm
EL=22.4GPa
EL=21.4GPa
Healthy/Young
EL=21.1GPa
EL=15.5GPa
Osteoporotic/Middle-aged
EL=19.3GPa
EL=11.2GPa
Osteoporotic/Old
Geometry and material properties
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Elastic properties - orthoptropy
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Methodology – key features
• Elastic properties of bone
• Orthotropic
• Vary with “age”
• Periosteum to endosteum variation
• Bone nonlinearity - strain-based plasticity
• Geometrical nonlinearity
• Contact interface (not tied)
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Arrangement of wires
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Results – yielded bone
Young Middle-aged Old
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Example 3: 2 and 4 wire fixation
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Illizarov wire results
Effect of wire tension on volume of yielded bone:
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IFM in 2 and 4 wire fixation i.e. construct stiffness
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Conclusions – Ilizarov fixators
Yielding never occurred through the entire cortical thickness
Bone displacement is independent of age
Increased yielding with ageing Increased wire tension reduces bone
yielding
Donaldson, Pankaj, Simpson, 2012, J Orth Res
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Locking plates
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Stiffness in literature
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Loading conditions
Ahmad et al. 2007
Hoffmeier et al. 2011
Uhl et al. 2008
Dunlap et al. 2011
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Our loading conditions
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Loading conditions
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An analytical tool for locking plates
MacLeod and Pankaj – Poster M292 – Monday
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Conclusions
MacLeod and Pankaj, 2014 — Comput Biomech Medicine, Springer
IFM
Patient-specific material properties – small role
Implant-bone interface modelling – small role
Device configuration (placement, number,
tension, materials) – major role
Nonlinear geometry – major role
Loading conditions – major role
Device loosening
All above
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Thank you
Francesc Levrero Florencio
Finn Donaldson*
Erica Sales
Krishna Manda
Rob Wallace Paul Jenkins
Shuqiao Zhu
Noel Conlisk*
Andrew Phillips*
Alisdair MacLeod* Chloe Scott
Jerome Goffin
Qing Ye
Adib Hamdani
Hamish Simpson Colin Howie
Marco Viceconti
Enrico Dall’Ara
Yuan Chen
John Clement
David Thomas Dave Cooper
Davide Ruffoni
Philipp Schneider
Alina Levchuk
Alexander Zwahlen Puneet Mahajan
Ralph Muller
Praveen Srivastava
Chaman Chandel
Suhail Ahmad