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Phil. Trans. R. Soc. A (2009) 367, 2079–2093
doi:10.1098/rsta.2008.0302
Multi-level patient-specific modelling of the
proximal femur. A promising tool to quantify
the effect of osteoporosis treatment
B Y L EEN L ENAERTS *
AND
G. H ARRY
VAN
L ENTHE
Division of Biomechanics and Engineering Design, Katholieke Universiteit
Leuven, Celestijnenlaan 300C, PB 2419, 3001 Leuven, Belgium
Preventing femoral fractures is an important goal in osteoporosis research. In order to
evaluate a person’s fracture risk and to quantify response to treatment, bone competence
is best assessed by bone strength. Finite-element (FE) modelling based on medical
imaging is considered a very promising technique for the assessment of in vivo femoral
bone strength. Over the past decades, a number of different FE models have been
presented focusing on the effect of several methodological aspects, such as mesh type,
material properties and loading conditions, on the precision and accuracy of these
models. In this paper, a review of this work is presented. We conclude that moderate to
good predictions can be made, especially when the models are tuned to specific loading
scenarios. However, there is room for improvement when multiple loading conditions
need to be evaluated. We hypothesize that including anisotropic material properties is
the first target. As a proof of the concept, we demonstrate that the main orientation of
the femoral bone structure can be calculated from clinical computed tomography scans.
We hypothesize that this structural information can be used to estimate the anisotropic
bone material properties, and that in the future this could potentially lead to a greater
predictive value of FE models for femoral bone strength.
Keywords: finite-element analysis; proximal femur; computed tomography;
anisotropy
1. Introduction
Osteoporosis is a very frequent skeletal disorder that is characterized by
compromised bone strength predisposing to an increased fracture risk (NIH
2000), mainly for the proximal femur, vertebrae and distal radius. Lifetime
fracture risk for women and men older than 50 rises to 40–50 and 13–22 per cent,
respectively (Johnell & Kanis 2005) and the resulting osteoporotic fractures
cause increased disability, morbidity and mortality (Cummings & Melton 2002).
Owing to ageing of the world population, osteoporosis will become not only an
increasing social but also an increasing economic problem. For example,
European medical care costs associated with osteoporosis were estimated at
* Author for correspondence ([email protected]).
One contribution of 15 to a Theme Issue ‘The virtual physiological human: tools and applications I’.
2079
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L. Lenaerts and G. H. van Lenthe
V31.7 billion in 2000 and are expected to rise to V76.7 billion in 2050 (Kanis &
Johnell 2005). This prognosis points out the importance of preventing and
reducing osteoporotic fractures.
Today, most treatment strategies for osteoporosis focus on medication, be it
antiresorptive such as bisphosphonates (Black et al. 2000), anabolic such as
parathyroid hormone (Bauer et al. 2006) or combination therapy (Bone et al.
2000). Recently, weight-bearing and high-impact exercises have received some
attention because of their potential effect on preserving bone density and
preventing osteoporosis (Heinonen et al. 1996). However, a major disadvantage
of these exercises is that they may increase the risk of fracture because of their
high impact. Therefore, other training methods with less fracture risk are being
sought. Whole-body vibration (WBV) is such an alternative method that
combines different aspects of impact exercises and power training. It has been
shown that high-frequency WBV can stop bone loss or even slightly increase
bone mass (Verschueren et al. 2004).
Regardless of what treatment strategy is used, its effect on bone quality should
ideally be evaluated in terms of bone strength, the best bone parameter to
quantify fracture risk. At present, bone mineral density (BMD) measurements
with dual-energy X-ray absorptiometry (DXA) are being used as a surrogate
measure of bone strength. However, the value of BMD measurements at a single
time point is limited, because it has been shown that bone density explains only
70 per cent of the variance in the mechanical properties of bone (Goulet et al.
1994). This result is in line with the finding that several clinical trials,
investigating the effect of antiresorptive drugs, have shown that BMD cannot
predict bone fracture risk accurately (Cummings et al. 2002; Graeff et al. 2007).
Furthermore, the difference between two consecutive BMD measurements
provides only poor indications of changes in femoral strength due to osteoporosis
treatment. A 40–50 per cent reduction in the risk of fracture through the use of
calcium and vitamin D supplementation has been reported, whereas BMD
increased by only 1 per cent (Chapuy et al. 1992; Dawson-Hughes et al. 1997).
Hence, although for large populations DXA measurement of BMD may provide
an indication of the changes in femoral strength induced by osteoporosis
treatments, for individual patients the predictive capacity of bone density is of
more limited value. BMD explained only approximately 4–28 per cent of the
35–50 per cent reduction in vertebral fracture risk after antiresorptive treatment
(Ettinger et al. 1999; Black et al. 2000; Reginster et al. 2000). Sarkar et al. (2002)
even found that BMD can explain only 4 per cent of the reduction in the
risk of vertebral fracture after a treatment with raloxifene; 96 per cent
remains unexplained.
Hence, other aspects such as bone geometry and its internal architecture need to
be considered too. Computed tomography (CT) is a methodology to measure both
density and structure in a single measurement. Especially, the combination with
finite-element (FE) analysis, the most widely used computational technique for
structural analysis in engineering, seems promising. CT-based FE models can
provide insights into load transfer through the bone architecture and with that help
our understanding of how differences in bone microarchitecture influence bone
strength. They integrate bone density measurements with bone geometry in a
three-dimensional fashion, thereby effectively compensating for the limitations of
DXA. As a consequence, CT-based FE models have been proposed as an alternative
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to BMD in the assessment of bone status. The potential of CT-based FE models has
been clearly recognized, and over the past years a number of papers have been
published on several methodological aspects of FE models, such as mesh type,
material properties, failure mechanisms and loading conditions.
In this paper, we will present a review of the different aspects of CT-based FE
modelling of the femur. Based on this review, we hypothesize that it could be
possible to improve the predictive value of these FE analyses for in vivo femoral
strength in individual patients. One of the aspects that has been rather
unexplored until now is the use of anisotropic material properties in a multi-level
FE model. We hypothesize that the anisotropic nature of bone is directly related
to trabecular orientation and that it is possible to derive it from clinical CT data.
The methodology to derive this trabecular orientation based on local grey values
will be described in this paper. In the future, it might be possible to use this
microstructural information to create patient-specific FE models that incorporate
anisotropic material properties in a hierarchical fashion.
2. Creating subject-specific models of the human femur
To build an FE model, a three-dimensional representation of the patient’s femur
is needed. Such a representation can be acquired from CT scan data. In short, the
different attenuation of X-rays by bone and soft tissue leads to images in which
different tissues have different grey values. After segmentation, the femoral bone
can be digitally extracted from the surrounding tissues. These segmented femur
slices are then joined together in a three-dimensional representation of the femur,
from which an FE model can be built. In the creation of accurate models, several
methodological aspects need to be addressed. These aspects are discussed below.
(a ) FE mesh
A basic concept in FE modelling is to divide a complicated object artificially
into a finite number of small and manageable pieces, so-called elements; the
behaviour of each element is described in mathematical equations, which are
then solved using computers. To generate an FE mesh from CT scan data, there
are basically two possible meshing techniques: geometry-based meshing and
voxel-based meshing. An example of both techniques is shown in figure 1.
(i) Geometry-based meshing
Geometry-based meshing encompasses a two-step procedure. First, the
contours of the femur are extracted from the CT scan. Based on these contours,
the surface of the bone is reconstructed, from which, in a second step, an FE
mesh with tetrahedral (Taddei et al. 2006) or hexahedral (Lotz et al. 1991a,b)
elements is built. Comparison of the two element types showed little difference
between the respective models (Ramos & Simoes 2006; Yosibash et al. 2007), as
long as the meshes are sufficiently well refined.
(ii) Voxel-based meshing
Voxel-based meshing (Keyak et al. 1990) comprises only one step: the
segmented voxel data are directly converted into an FE model with brick
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L. Lenaerts and G. H. van Lenthe
(a)
(b)
Figure 1. (a) Geometry-based finite-element mesh with tetrahedral elements and (b) voxel-based
finite-element mesh with brick elements.
elements, hence meshes can be generated very quickly. The main advantage of
this approach is that it is a simple automated technique that can deal with any
shape of arbitrary complexity. However, curved surfaces cannot be represented
properly by brick elements. Hence, where voxel meshes can provide accurate
internal stresses and strains, the jagged-edged surface causes peak stresses and
strains, which is a disadvantage when accurate surface stresses and strains are
needed. Post hoc smoothing algorithms can alleviate these surface errors
(Charras & Guldberg 2000). A more fundamental approach consists in
smoothing the surface before performing the actual FE calculations. However,
conflicting results have been reported. Where Boyd & Muller (2006) showed
that peak stresses on the surface of a sphere model were lowered by a factor of 4
after smoothing, Arbenz & Flaig (2007) found only minimally lower stresses for
a similar model. Hence, further investigation on smoothing of voxel-based
meshes is needed.
Only a limited number of voxel-based models for the prediction of femoral
bone strength have been presented. Hence, it seems that the full potential
of voxel-based meshes has not been explored. However, the ease in generating
voxel models, combined with the recent massive increase in speed and size of
very large FE models, appear to provide good conditions for further exploration.
Unlike macroscopic-scale CT-based FE modelling, voxel meshes are already
generally accepted as a meshing technique for mCT-based modelling of trabecular
bone. For these complex structures, it has been shown that tetrahedral and
voxel-based meshes yield almost identical results at higher resolutions (Ulrich
et al. 1998) and that there is a very good agreement between the predictions of a
voxel-based mFE model and experimental measurements (Ladd et al. 1998; Kabel
et al. 1999; Homminga et al. 2003).
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(b ) Material properties
The use of appropriate material properties in FE analyses has received a lot of
attention. Today, it has become the state of the art to take into account the
inhomogeneity of bone, based on CT numbers, the attenuation coefficient of the
material present in each voxel. Depending on the research question, a number of
relationships need to be defined, which include: the conversion of CT number to
elastic modulus; the use of a linear or a nonlinear stress–strain relationship; the
assignment of isotropic or anisotropic material properties; and the implemented
failure theory. When generating such heterogeneous models, it has to be noted
that in some FE codes only a limited number of materials can be defined for each
model. Therefore, the number of materials often gets reduced by dividing the
range of elastic moduli into a limited number of intervals (Zannoni et al. 1998).
(i) From CT number to Young’s modulus
To estimate the elastic modulus of the elements based on CT numbers, a twostep procedure is normally taken: (i) convert the CT numbers into bone density
and (ii) estimate Young’s modulus from bone density.
Bone density can be directly assessed from the CT scan data by its linear
correlation with the CT number (McBroom et al. 1985). This relationship can be
obtained by scanning a phantom, with known material densities, together with
the patient. Bone density is often expressed as apparent density, which is
calculated by dividing the bone’s wet weight by the total volume of the bone
under investigation, including its pores. Also wet density, calculated as the wet
weight divided by bone tissue volume, and ash density, calculated as ash weight
over bone tissue volume, are being used.
In contrast to the first step in the conversion of CT data into material
properties, the relationship between bone density and bone elasticity is less clear.
In a recent literature review, Helgason et al. (2008) have investigated density–
elasticity relationships derived from experimental tests. After selecting and
normalizing 22 relationships (figure 2), they found considerable differences
between the investigated studies that could only be partially explained by
methodological inter-study differences such as boundary conditions and specimen
geometry. Morgan et al. (2003) investigated whether site specificity exists in the
density–elasticity relationships. They tested on-axis bone samples from different
anatomical sites with a protocol that minimized the end artefacts. They found
that, when the site-specific trabecular structure was taken into account, there
were no inter-site differences in tissue moduli. Hence, the differences in the
Young’s modulus–density relationship are due to site-specific variations in
trabecular architecture. This implies that, in the FE modelling of bone, sitespecific density–elasticity relationships should be used.
(ii) Isotropic or anisotropic properties
It is generally known that bone material, cortical as well as cancellous, is
orthotropic rather than isotropic (Yang et al. 1998). Cortical or compact bone is
the dense material that forms the outer shell of the bone. Cancellous or
trabecular bone is the porous structure enclosed by the cortical shell. The main
orientations of this structure appear adapted to the main loading directions,
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L. Lenaerts and G. H. van Lenthe
20
18
16
14
E (GPa)
12
10
8
6
Carter & Hayes (1977)
Lotz et al. (1990)
Snyder & Schneider (1991)
Lotz et al. (1991)
Hodgkinson & Currey (1992)
Linde et al. (1992)
Anderson et al. (1992)
Dalstra et al. (1993)
Keller (1994) — spine
Keller (1994) — femur
Keyak et al. (1994)
Goulet et al. (1994)
Keaveny et al. (1997)
Li & Aspden (1997)
Ouyang et al. (1997)
Kopperdahl et al. (1998)
Ciarelli et al. (2000)
Morgan et al. (2003) — vertebra
Morgan et al. (2003) — prox. tibia
Morgan et al. (2003) — greater troch.
Morgan et al. (2003) — femoral neck
Kaneko et al. (2004)
4
2
0
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
1.8
2.0
Figure 2. Young’s modulus versus apparent wet density for all studies included in the study of
Helgason et al. (2008). (Reprinted with permission from Clinical Biomechanics. Copyright q Elsevier.)
a feature known as Wolff’s Law. A meta-analysis showed that, for trabecular
bone samples, approximately 64 per cent of the variabilities in bone stiffness and
strength are determined by its apparent density (Wehrli et al. 2002). Bone
microstructure explains a significant part of the remaining variation: by
including architectural parameters in the analysis, the predictive power increases
to over 90 per cent for bone stiffness (Turner et al. 1990; van Rietbergen et al.
1998; Yang et al. 1998). Nevertheless, in most femoral FE models of the human
femur, isotropic material properties are assigned to the elements (Keyak et al.
1990; Lotz et al. 1991a; Taddei et al. 2006). This approach is driven by the fact
that CT scans provide only information about bone density in every voxel, and
do not contain information on local anisotropy. Two recent studies have
indicated that this is an appropriate methodology (Peng et al. 2006; Baca et al.
2008). By comparing models with isotropic and orthotropic material properties,
Peng et al. and Baca et al. concluded that they gave very similar results.
However, it has to be noted that Peng et al. did not vary the orthotropic
orientation for each element; instead, they assumed that the principal directions
of the bone are identical throughout the entire bone. Baca et al. introduced more
diversity by defining 30 orthotropic orientations. However, for each element,
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the orientations were based on the organization of the central vascular canals of
the Haversian lamellar systems in the cortical bone. Therefore, they gave an
indication of the anisotropic structure of cortical bone but not of the internal
trabecular architecture. Hence, none of these models represent local anisotropy of
the more complex internal architecture and it is questionable whether their
conclusions hold. In addition, the small differences between the isotropic and
anisotropic models that were observed could also be related to the applied
loading. Both Peng et al. (2006) and Baca et al. (2008) were simulating stance
phase, which is a physiological loading condition. Verhulp et al. (2006) suggested
that the transverse modulus, introduced by assigning orthotropic material
properties, does not play a very important role when physiological loading
conditions such as single-legged stance are considered, since bone architecture is
adapted to those conditions, but that differences between the isotropic and
orthotropic models can be substantially larger for non-physiological loading such
as falls to the side.
(iii) Nonlinear models and failure theories
At yielding and subsequent failure, bone loading is no longer linearly related to
displacement and may show post-yielding behaviour (Keaveny et al. 2001) or
immediate failure (Schileo et al. 2008). In most studies, however, linear material
properties are assigned to the elements of FE models to predict the load at which
a femur will break and the location where a failure will initiate. The use of these
linear models is in principle limited, because they are valid only up to the onset of
local bone failure, whereas the use of nonlinear models is also justified past the
onset of bone failure until total failure.
Lotz et al. investigated the predictive value for fracture load of linear (Lotz
et al. 1991a) and nonlinear (Lotz et al. 1991b) models. In addition, they studied
the predictive value for femoral fracture load of stress- and strain-based theories
in linear models (Lotz et al. 1991a). For both linear and nonlinear models, they
found a very good agreement between measured and calculated fracture loads.
Additionally, the nonlinear model could predict bone yielding well. The results
also showed that application of a strain-based failure criterion improved the
prediction of the failure load. Similar to Lotz et al., Schileo et al. (2008) found
that strain-based criteria resulted in a better prediction of the fracture onset and
the level of failure risk. These results seem obvious, as it is generally accepted
that strain parameters describe bone failure. However, in another comparative
study of failure theories, Keyak & Rossi (2000) found just the opposite after
investigating eight different failure theories; when using linear FE models, stressbased theories predicted fracture load better than strain-based theories. Hence,
no consensus seems to exist on which failure criterion predicts femoral fracture
load best.
Keyak and co-workers also investigated the effect of linear and nonlinear
material properties on the accuracy of their FE models. For the linear models,
they found significant relationships between the measured and predicted fracture
loads (r 2Z0.75 for single-legged stance; r 2Z0.90 for fall; Keyak et al. 1998). For
a single-legged stance, precision for identifying subjects with below-average
fracture loads was found to be insufficient. FE models with nonlinear material
properties overcame this problem (Keyak 2001). These models were, in contrast
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L. Lenaerts and G. H. van Lenthe
to the linear models, precise enough to identify 20 per cent of the subjects with
below-average fracture loads with a reliability of at least 97.5 per cent. Hence,
these data show that, by using nonlinear material properties, more subjects at
risk of breaking a hip could be identified, especially those with low fracture loads,
who are at the greatest risk.
(c ) Loading conditions
Fractures occur when the loads acting on a bone are higher than the strength
of that bone. Hence, besides an accurate assessment of bone strength,
information on the loading conditions that encompass the greatest risk of
femoral fracture is also needed.
Most femoral fractures are caused by a fall to the side, but spontaneous
fractures also account for a significant amount of hip fractures. Consequently,
one or both of these situations are simulated in FE analyses, by modelling the
impact of a fall to the side and/or by modelling single-legged stance (Lotz et al.
1991a,b; Keyak et al. 1998). Keyak et al. confirmed the importance of these two
loading conditions by studying the effect of loading direction on the femoral
fracture load. They found that fracture load (i.e. bone strength) depends on the
direction of applied loading; furthermore, they found that fracture loads were the
smallest for three loading conditions in particular: a fall to the posterolateral
aspect of the femur; single-legged stance; and stair climbing (Keyak et al. 2001).
They also showed that the fracture load for one of these loading conditions
accounts for only 81 per cent of the variance in the fracture load for the other
loading condition, leaving 19 per cent unexplained (Keyak 2000). This is
probably related to the complex geometry and internal trabecular architecture of
the bone, and indicates the limitations of isotropic material properties.
Although abnormal contraction of major rotator muscles could induce hip
fracture (Yang et al. 1996), it has been shown that excluding the abductor muscle
forces in FE models does not have a notable effect on the predicted femoral
strength and fracture location (Keyak et al. 2005; Cristofolini et al. 2007). Hence,
muscle forces can be omitted in FE models of femoral bone strength.
3. Experimental validation
The gold standard of measuring bone stiffness and strength is through
biomechanical testing. Hence, FE models are best validated by mechanical
testing of the bone under investigation. Of course, the boundary conditions as
applied in these FE models should mimic the loads as applied in the experimental
set-up. Depending on which research question is to be answered, different set-ups
simulating different loading conditions have been investigated. It is beyond the
scope of this paper to discuss all these different set-ups in detail, and to comment
on their precision and accuracy. Taking into account all inter-study differences,
it is difficult to compare the accuracy of one FE model relative to another and to
determine the effect of the number of femurs used on the accuracy of the model.
To the authors’ knowledge, the most rigorous validation in the prediction of
femoral fracture risk was performed by Keyak et al. (2005). They used a group of
18 femora to fine-tune their parameter settings. Afterwards, a group of 26 other
femora were used to evaluate the accuracy and the precision of the model. They
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found that the second group predicted fracture load for a single-legged stance
with nearly the same accuracy (r 2Z0.88 and 0.83, respectively) as the
development group.
4. Improved capacity for anisotropic FE models?
In most FE models heterogeneous isotropic material properties are assumed,
accounting for the bone density distribution but not for internal architecture.
However, it has been shown that the accuracy and the precision of these isotropic
models for the prediction of femoral strength are limited (Cody et al. 1999; Keyak
et al. 2005). This is most probably due to the fact that bone is orthotropic rather
than isotropic.
The orthotropic nature of the internal trabecular architecture is a very
important structural parameter that contributes to the global femoral strength.
However, from the preceding literature review, it can be deduced that only very
little research has been undertaken on anisotropic FE modelling of the proximal
femur. Studies that did account for anisotropy either used methods to derive
internal architectural parameters that cannot be used in vivo for patient-specific
models (Wirtz et al. 2003) or assigned orthotropic material properties to all
elements in a global coordinate system not accounting for local differences in
orthotropy (Taylor et al. 2002; Peng et al. 2006). Hence, no valid method
exists to derive local anisotropy parameters in vivo. As a consequence, it
has not been possible to analyse patient-specific FE models with anisotropic
material properties. However, based on our review, we hypothesize that
inclusion of local anisotropy could help to improve the prediction of femoral
strength in vivo.
5. Determination of trabecular orientation from clinical CT scans
The anisotropic nature of trabecular bone is directly related to the anisotropic
trabecular architecture. Unfortunately, state-of-the-art technology does not
allow detailed images of the trabecular architecture. This begs for the
development of multi-level technology to incorporate anisotropic properties.
In this section, the methodology to derive trabecular orientation from clinical CT
data and the concept of applying these data to build patient-specific FE models
are described. An overview of this procedure is represented in figure 3.
From clinical CT scans, it is not possible to derive trabecular architecture in
detail. However, the distribution of the grey values in the images gives an
indication of the main orientations of the internal structure, containing a
measure of local anisotropy. To derive these orientations from two-dimensional
CT slices, the method of line fraction deviation (LFD) developed by Geraets
(1998) for small square areas was extended in order to be applicable to CT scans
of whole femora. This new method scans the image with a squared region of
interest, for which the LFD is calculated in every pixel. As the region of interest
covers several pixels in every position, orientation will be calculated more than
once for every pixel. The resulting orientation for a pixel is calculated by
averaging all orientations found for that pixel.
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density
orthotropic
material properties
CT number
trabecular and
cortical
orientation
Figure 3. Overview of the proposed procedure to derive orthotropic material properties from CT
scan data.
(a)
(b)
(c)
Figure 4. (a) Slice of a CT scan of a 63-year-old woman, (b) distribution of bone density in the
segmented femur and (c) local orientation of bone structures calculated with new method,
extending the LFD method of Geraets (1998).
As a proof of principle, we applied our method to a frontal slice of an in vivo
CT scan of the right femur of a 63-year-old woman (Siemens Sensation
16; 120 kV; 140 mA s; pixel size 1 mm), participating in a study on whole-body
vibration training (Verschueren et al. 2004). The participant gave her informed
written consent before enrolment. The CT data were segmented (figure 4a,b) and
the cortical bone was separated from the trabecular bone. Subsequently, the
trabecular and cortical parts of the bone were analysed separately. During the
analysis of the trabecular bone, a 10!10 pixel region of interest was moved over
the slice, calculating the local orientation for each pixel with the LFD method.
For the cortical bone, the region of interest was 12!12 pixels. As the cortical
bone of the proximal femur is only a shell, this region of interest would often
include soft tissue and cortical bone. Therefore, the grey values of the
surrounding tissues were set to zero during segmentation. Subsequently, the
LFD was calculated only for those regions of interest that contained pixels
representing cortical bone. The results of these analyses (figure 4c) show good
correspondence with the structural orientations in the regions where structure is
most pronounced, which is the entire cortical bone, and the trabecular bone in
the femoral head. In the other parts of the femur, structural orientations were
less definable. This is probably due to the limited resolution of the CT scan, as
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the pixel size (1!1 mm) is too large to capture the thin trabecular bone
structures. At the borders of the cortical bone, some calculated orientations differ
from the expected direction, probably due to partial volume effects.
6. Towards improved prediction of bone strength
The newly developed method is now being expanded to three dimensions in order
to calculate the spatial orientation of bone tissue for every voxel of a femur.
These orientations can then be incorporated into multi-level FE models of the
femur, as they account for local anisotropy based on structural parameters at the
microscopic scale. These might have the potential to predict femoral strength
more accurately than isotropic FE models because they mimic the structural and
material properties of the bone better.
With the recent introduction of a new generation of clinical CT scanners that
are able to image the proximal femur with a resolution of 0.3–0.4 mm isotropic
resolution, there is potential to refine the assessment of trabecular orientation,
although it will still not be good enough to visualize individual trabeculae.
A single trabecula can be imaged with high-resolution peripheral quantitative
CT (HR-pQCT, XtremeCT, Scanco Medical), providing an isotropic resolution
of 80 mm. Unfortunately, this scanning technique is applicable only to
‘peripheral’ bones.
7. Conclusion
As osteoporosis is becoming an increasing social and economic problem (Kanis &
Johnell 2005), a diagnostic tool that can detect osteoporosis in an early stage and
predict bone strength accurately in individual patients is indispensable. Also, in
view of studying the effects of new osteoporosis treatments, a method is needed
that is more sensitive to small changes in bone strength than DXA, the current
standard diagnostic tool. Rather than using a large population of patients for a
long time until osteoporotic fracture appears, such a technique should allow for
a much earlier detection of changes in fracture risk. FE modelling is such a
technique. CT-based FE models provide the means for a mechanistic
understanding of bone strength, which goes beyond the statistical correlations
based on BMD; they have the potential to become a standard medical tool in the
prediction of femoral strength, the best parameter to quantify fracture risk (Cody
et al. 1999). The aim of this study was to review the current standing in FE
modelling of the human femur and to identify which aspects of the modelling
procedure should be improved to enhance the predictive value of FE models in
the diagnosis of osteoporosis.
It was found that geometry-based as well as voxel-based FE models can
provide moderate to good predictions of femoral bone strength, especially when
the models are tuned to specific loading scenarios. However, there is room for
improvement when multiple loading conditions need to be evaluated.
Improvement may be achieved by a better description of bone failure, as no
standardized algorithms exist for assessing fracture from FE analyses. It should
be noted that the precise failure mechanisms and associated material properties
are still not well understood.
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L. Lenaerts and G. H. van Lenthe
This review also showed that very few studies addressed the problem of
incorporating local anisotropy into their models and those that did based the
anisotropic parameters on a priori assumptions (Taylor et al. 2002; Wirtz et al.
2003; Peng et al. 2006; Baca et al. 2008). For that purpose, we developed a new
method that analyses two-dimensional slices of a CT scan using the LFD.
Although the results of the analysis are promising, this method has some
limitations. First, the results can vary through changes in the size of the region of
interest. To find an optimal size for this region of interest, more CT images need
to be analysed. Second, for now, only two-dimensional images can be analysed.
At the moment, the method is being extended to three-dimensional images.
Third, the method could not be validated, as no detailed information on the main
orientations of the bone was available. However, visual inspection could be
considered as a first validation. In a region similar to the femoral head, where the
orientation of the bone is clearly visible, the new method is able to detect this
direction. Fourth, owing to the limited resolution of the CT scan, it is not
possible to distinguish trabecular structures in some parts of the femur. Hence, it
is also difficult to calculate the main direction in those parts. However, with
modern CT scanners, patients can be scanned with a higher resolution, as a
result of which the orientation of the internal structures will be better visible and
can be calculated more accurately. Hence, we expect that, when extended to
three-dimensional images, the newly developed method will be able to calculate
the spatial orientation of the bone tissue in every voxel of a high-resolution CT
scan. These orientations can then be integrated as anisotropic material
properties into a heterogeneous FE model. As local orientations are structural
parameters on the microscopic level, which will be incorporated into a
macroscopic femur model, the development of these types of model might
be the beginning of the use of multi-level technology in the in vivo prediction
of femoral strength. We hypothesize that the predictive value for fracture risk
of the femur will be improved by including the calculated anisotropic
material properties.
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