Medical imaging technologies have allowed for in vivo evaluation of the human musculoskeletal system. With advances in both medical imaging and computing, patient-specific model development of anatomic structures is becoming a reality. Three-dimensional surface models are useful for patient-specific measurements and finite element studies. Orthopaedics is closely tied to engineering in the analysis of injury mechanisms, design of implantable medical devices, and potentially in the prediction of injury. However, a disconnection exists between medical imaging and orthopaedic analysis; whereby, the ability to generate three-dimensional models from an imaging dataset is difficult, which has restricted its application to large patient populations. We have compiled image processing, image segmentation, and surface generation tools in a single software package catered specifically to image-based orthopaedic analysis. We have also optimized an automated segmentation technique to allow for high-throughput bone segmentation and developed algorithms that help to automate the cumbersome process of mesh generation in finite element analysis. We apply these tools to evaluate graft placement in anterior cruciate ligament reconstruction in a multicenter study that aims to improve the patient outcomes of those that undergo this procedure.

A number of methods for adaptation of existing all-hexahedral grids by localized refinement have been developed; however, none ideally fit all refinement needs. This thesis presents the structure to a method of two-refinement developed for conformal, structured, all-hexahedral grids that offers flexibility beyond what has been offered to date. The method is fundamentally based on pillowing pairs of sheets of hexes. This thesis also suggests an implementation of the method, shows the results of examples refined using it and compares these results to results from implementing three-refinement on the same examples.