Statistical Shape Modeling of Proximal Femur Bone Towards Hip Implant Design Optimization

Abstract

The femur bone is the major loadbearing skeletal component in the human body and its proximal end is prone to either osteoporosis or high-energy injury related fractures. When fracture occurs, prosthetic implants depending on the type and level of injury are used to restore the normal biomechanics of the bone. Fracture fixation and subsequent bone healing process depend on several factors including the anatomical fitting quality of the prosthetic implant utilized. However, the standard commercially available prosthetic implants are generic and commonly implants are designed and validated based on a limited set of cadaver bones which may result unfitting of implants to all patient groups that may cause implant related complications. In recent years, generic implants have been optimized for a specific population group based on anatomical data obtained from the target population to improve the fitting quality of implants to the intended anatomical region. The aim of this thesis was to develop a statistical shape model (SSM) of the proximal femur bone and to carry out morphometric analysis of the femoral neck shaft angle (NSA). Statistical shape modeling has the potential to accurately capture anatomical shape variabilities from a set of shape instances to build a flexible shape model. The SSM of the proximal femur was developed from training datasets imaged via CT scanning. The image data were collected from Jimma University Medical Center, ALERT Hospital, and MCM General Hospital. The shape modelling process followed sequential steps including semi automatic image segmentation and 3D surface reconstruction, manual annotation of anatomical landmarks, establishment of surface correspondence across datasets, and model building using principal component analysis. As well as, robust femoral NSA measurements are implemented. The quality of the SSM was validated using shape quality metrics: compactness, specificity, and generality. The validation result showed that the first eleven principal components of the SSM represented 92.85% of the total variance in the model. Similarly, the specificity and generalization of the model were approximately 1.33mm and 0.83mm respectively. In the future, the full length shape model of the femur and its morphometric parameters could be developed to further improve the hip rotation and flexion as per the clinical standards and to enhance a reliable measurement of femoral head diameter, horizontal offset, and femoral neck-anteversion angle.