Assessment of the restoration of load-bearing function is the central goal in the study of bone fracture healing. However, bone fracture calluses are inhomogeneous and irregular materials and this complexity has led to considerable uncertainty in the assessment of biomechanical property improvement or impairment during various therapeutic interventions and genetic models of pathological fracture healing. Unfortunately, as a result, arguably one of the most important criteria, mechanical stability, is the least resolved with respect to fracture healing assessment. To address this issue, an inverse finite element analysis (FEA) approach was developed in which biomechanical testing and microCT imaging are integrated through the use of computational modeling to determine mechanical properties of the healing fracture callus tissue. The presented work serves to evaluate the inverse analysis as a functional fracture healing assessment methodology in comparison to more traditional imaging and biomechanical testing measures within the context of normal fracture healing and a therapeutic system involving mesenchymal stem cell (MSC) transplantation. As compared to traditional fracture healing metrics, the results demonstrate that the inverse FEA approach: (1) was the only metric to successfully detect both longitudinal and therapeutic responses, and (2) performed significantly better at late-stage healing time points, where traditional metrics failed. The inverse analysis also added insight to the role of MSCs in fracture healing by demonstrating both accelerated healing and therapeutic benefit at late-stage healing. Additionally, a systems-based approach was developed for the generation of ease-of-use enhancements to the inverse analysis methodology in order to facilitate a wider usage among bone fracture biology groups whom are not experts in computational analysis. This was accomplished by the construction of an online web-enabled model submission system in which bone fracture callus microCT imaging and biomechanical testing data are collected with a minimal amount of pre-processing on a remote user node and submitted to a compute node which builds and executes the inverse model for material property reconstruction. In conclusion, the inverse FEA approach is shown to be a sensitive and functional fracture healing measure and provides a significant first-step towards normalizing the often challenging process of assessing mechanical integrity of healing fractures.
| Identifer | oai:union.ndltd.org:VANDERBILT/oai:VANDERBILTETD:etd-08112011-105508 |
| Date | 23 August 2011 |
| Creators | Weis, Jared Anthony |
| Contributors | Craig Duvall, Hak-Joon Sung, John Gore, Michael Miga, Anna Spagnoli |
| Publisher | VANDERBILT |
| Source Sets | Vanderbilt University Theses |
| Language | English |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | http://etd.library.vanderbilt.edu/available/etd-08112011-105508/ |
| Rights | unrestricted, I hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Vanderbilt University or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report. |
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