Effect of growth plate geometry and growth direction on prediction of proximal femoral morphology

AbstractMechanical stimuli play a significant role in the process of endochondral growth. Thus far, approaches to understand the endochondral mechanical growth rate have been limited to the use of approximated location and geometry of the growth plate. Furthermore, growth has been simulated based on the average deflection of the growth plate or of the femoral neck. It has also been reported in the literature that the growth plate lies parallel to one of the principal stresses acting on it, to reduce the shear between epiphysis and diaphysis. Hence the current study objectives were (1) to evaluate the significance of a subject-specific finite element model of the femur and growth plate compared to a simplified growth plate model and (2) to explore the different growth direction models to better understand proximal femoral growth mechanisms. A subject-specific finite element model of an able-bodied 7-year old child was developed. The muscle forces and hip contact force were computed for one gait cycle and applied to a finite element model to determine the specific growth rate. Proximal femoral growth was simulated for two different growth direction models: femoral neck deflection direction and principal stress direction. The principal stress direction model captured the expected tendency for decreasing the neck shaft angle and femoral anteversion for both growth plate models. The results of this study suggest that the subject-specific geometry and consideration of the principal stress direction as growth direction may be a more realistic approach for correct prediction of proximal femoral growth morphology

The Development And Application Of A Statistical Shape Model Of The Human Craniofacial Skeleton

Biomechanical investigations involving the characterization of biomaterials or improvement of implant design often employ finite element (FE) analysis. However, the contemporary method of developing a FE mesh from computed tomography scans involves much manual intervention and can be a tedious process. Researchers will often focus their efforts on creating a single highly validated FE model at the expense of incorporating variability of anatomical geometry and material properties, thus limiting the applicability of their findings. The goal of this thesis was to address this issue through the use of a statistical shape model (SSM). A SSM is a probabilistic description of the variation in the shape of a given class of object. (Additional scalar data, such as an elastic constant, can also be incorporated into the model.) By discretizing a sample (i.e. training set) of unique objects of the same class using a set of corresponding nodes, the main modes of shape variation within that shape class are discovered via principal component analysis. By combining the principal components using different linear combinations, new shape instances are created, each with its own unique geometry while retaining the characteristics of its shape class. In this thesis, FE models of the human craniofacial skeleton (CFS) were first validated to establish their viability. A mesh morphing procedure was then developed to map one mesh onto the geometry of 22 other CFS models forming a training set for a SSM of the CFS. After verifying that FE results derived from morphed meshes were no different from those obtained using meshes created with contemporary methods, a SSM of the human CFS was created, and 1000 CFS FE meshes produced. It was found that these meshes accurately described the geometric variation in human population, and were used in a Monte Carlo analysis of facial fracture, finding past studies attempting to characterize the fracture probability of the zygomatic bone are overly conservative

Design and Validation of Automated Femoral Bone Morphology Measurements in Cerebral Palsy

Accurate quantification of bone morphology is important for monitoring the progress of bony deformation in patients with cerebral palsy. The purpose of the study was to develop an automatic bone morphology measurement method using one or two radiographs. The study focused on four morphologic measurements—neck-shaft angle, femoral anteversion, shaft bowing angle, and neck length. Fifty-four three-dimensional (3D) geometrical femur models were generated from the computed tomography (CT) of cerebral palsy patients. Principal component analysis was performed on the combined data of geometrical femur models and manual measurements of the four morphologic measurements to generate a statistical femur model. The 3D–2D registration of the statistical femur model for radiography computes four morphological measurements of the femur in the radiographs automatically. The prediction performance was tested here by means of leave-one-out cross-validation and was quantified by the intraclass correlation coefficient (ICC) and by measuring the absolute differences between automatic prediction from two radiographs and manual measurements using original CT images. For the neck-shaft angle, femoral anteversion, shaft bowing angle, and neck length, the ICCs were 0.812, 0.960, 0.834, and 0.750, respectively, and the mean absolute differences were 2.52°, 2.85°, 0.92°, and 1.88 mm, respectively. Four important dimensions of the femur could be predicted from two views with very good agreement with manual measurements from CT and hip radiographs. The proposed method can help young patients avoid instances of large radiation exposure from CT, and their femoral deformities can be quantified robustly and effectively from one or two radiograph(s)

Design and Validation of Automated Femoral Bone Morphology Measurements in Cerebral Palsy

Accurate quantification of bone morphology is important for monitoring the progress of bony deformation in patients with cerebral palsy. The purpose of the study was to develop an automatic bone morphology measurement method using one or two radiographs. The study focused on four morphologic measurements-neck-shaft angle, femoral anteversion, shaft bowing angle, and neck length. Fifty-four three-dimensional (3D) geometrical femur models were generated from the computed tomography (CT) of cerebral palsy patients. Principal component analysis was performed on the combined data of geometrical femur models and manual measurements of the four morphologic measurements to generate a statistical femur model. The 3D-2D registration of the statistical femur model for radiography computes four morphological measurements of the femur in the radiographs automatically. The prediction performance was tested here by means of leave-one-out cross-validation and was quantified by the intraclass correlation coefficient (ICC) and by measuring the absolute differences between automatic prediction from two radiographs and manual measurements using original CT images. For the neck-shaft angle, femoral anteversion, shaft bowing angle, and neck length, the ICCs were 0.812, 0.960, 0.834, and 0.750, respectively, and the mean absolute differences were 2.52A degrees, 2.85A degrees, 0.92A degrees, and 1.88 mm, respectively. Four important dimensions of the femur could be predicted from two views with very good agreement with manual measurements from CT and hip radiographs. The proposed method can help young patients avoid instances of large radiation exposure from CT, and their femoral deformities can be quantified robustly and effectively from one or two radiograph(s).Y