Defense Date


Document Type


Degree Name

Doctor of Philosophy


Biomedical Engineering

First Advisor

Jennifer Wayne


The use of computational modeling is an increasingly commonplace technique for the investigation of biomechanics in intact and pathological musculoskeletal systems. Moreover, given the robust and repeatable nature of computer simulation and the prevalence of software techniques for accurate 3-D reconstructions of tissues, the predictive power of these models has increased dramatically. However, there are no patient-specific kinematic models whose function is dictated solely by physiologic soft-tissue constraints, articular shape and contact, and without idealized joint approximations. Moreover, very few models have attempted to predict surgical effects combined with postoperative validation of those predictions. Given this, it is not surprising that the area of foot/ankle modeling has been especially underserved. Thus, we chose to investigate the pre- and postoperative kinematics of Adult Acquired Flatfoot Deformity (AAFD) across a cohort of clinically diagnosed sufferers. AAFD was chosen as it is a chronic and degenerative disease wherein degradation of soft-tissue supporters of the medial arch eventually cause gross malalignment in the mid- and hindfoot, along with significant pain and dysfunction. Also, while planar radiographs are still used to diagnose and stage the disease, it is widely acknowledged that these 2-D measures fail to fully describe the 3-D nature of AAFD. Thus, a population of six patient-specific rigid-body computational models was developed using the commercially available software packages Mimics® and SolidWorks® in order to investigate foot function in patients with diagnosed Stage IIb AAFD. Each model was created from patient-specific sub-millimeter MRI scans, loaded with body weight, individualized muscle forces, and ligament forces, in single leg stance. The predicted model kinematics were validated pre- and postoperatively using clinically utilized radiographic angle distance measures as well as plantar force distributions. The models were then further exploited to predict additional biomechanical parameters such as articular contact force and soft-tissue strain, as well as the effect of hypothetical surgical interventions. Subsequently, kinematic simulations demonstrated that the models were able to accurately predict foot/ankle motion in agreement with their respective patients. Additionally, changes in joint contact force and ligament strain observed across surgical states further elucidate the complex biomechanical underpinnings of foot and ankle function.


© The Author

Is Part Of

VCU University Archives

Is Part Of

VCU Theses and Dissertations

Date of Submission

December 2013