ARUP; ISBN: 978-0-9562121-5-3 - CMBBE 2012 - Cardiff University

problem is time consuming, which limits this approach to a small number of cases. In
addition, most analyses do not account for the wide variation in material properties and
geometry that may occur in natural tissues or manufacturing imperfections in synthetic
materials since the underlaying information came from only a limited number of
anatomical structures. Recent work started to include this variability into biomechanical
analyses; Taddei et al. [4] proposed a method based on Monte Carlo simulations to
include geometric and material uncertainties in finite element simulations. The
technique was improved by Laz et al. [5] who proposed a probabilistic platform to
account for the uncertainties in the mechanical properties for fracture risk predictions.
Khalaji et al [6]. proposed to combine finite element calculations with statistical shape
models (SSM). SSM has been used to represent human bone anatomy [7, 8, 9]. This
method was extended to include bone mineral content in a combined statistical model of
appearance (SMA) [3], which includes both anatomical and mechanical information.
The purpose of the present work is to propose a technique to reduce the computational
time to build and solve patient specific models. The proposed method relies on a
combination of statistical model of appearance and finite element analysis.
3. METHODS
3.1 Statistical model of appearance
The SMA was built from a training set of 72 female left femur CT image series. First,
the femurs were manually segmented in Amira (Visage Imaging Inc., USA) and the
surface mesh was created. Subsequently, correspondence and non-rigid alignment
between the 72 training instances was established using a morphing approach developed
by ANSYS Inc., France. The ANSYS morpher takes as input a generic template mesh
and a subject's geometry. Additionally, ten anatomical landmark points on the two
meshes were recorded. Using these inputs, the algorithm based on radial basis function
matches the template mesh onto the subject's geometry and generates a new volume
mesh of the subject geometry, with corresponding number of nodes and elements [10].
Principle component analysis (PCA) [8] was used to create the SSM from the
volumetric meshes. A second statistical model for the intensities (SIM) of the CT
images was created using PCA. Since the mineral content is related to the image
intensity, this model corresponds to a statistical mechanical model of the bones. Both
models were combined to build the SMA. The SMA was used to generate 1025 femur
mesh instances, using the first five modes covering 75% of the variability of the training
population. 1000 bones were used to build the training population for the FE predictions
and 25 cases are used for the model validation.
3.2 Finite element model
An FE model was built for each femur of the dataset with a loading situation
corresponding to normal walking [11]. The magnitude of the forces was scaled
according to the subject's body weight. To account for the individuality of each subject,
the body weight was generated based on the subject’s femur length [3] and on the
training population's body mass index (26.36 ± 6.01). The loading was combined with