The finite element method (FEM) has been widely applied to various medical imaging applications over the past two decades. The remarkable progress in high-resolution imaging techniques has allowed FEM to draw great research interests in computing trabecular bone (TB) stiffness from three-dimensional volumetric imaging. However, only a few results are available in literature on applying FEM to multi-row detector CT (MDCT) imaging due to the challenges posed by limited spatial resolution. The research presented here develops new methods to preserve TB structure connectivity and to generate high-quality mesh representation for FEM from relatively low resolution images available at MDCT imaging. Specifically, it introduced a space-variant hysteresis algorithm to threshold local trabecular structure that preserves structure connectivity. Also, mesh generation algorithms was applied to represent TB micro-architecture and mesh quality was compared with that generated by traditional methods. TB stiffness was computed using FEM simulation on micro-CT (µ-CT) and MDCT images of twenty two cadaveric specimens of distal tibia. Actual stiffness of those specimens were experimentally determined by mechanical testing and its correlation with computed stiffness was analyzed. The observed values of linear correlation (r2) between actual bone stiffness and computed stiffness from µ-CT and MDCT imaging were 0.95 and 0.88, respectively. Also, reproducibility of the FEM-based computed bone stiffness was determined from repeat MDCT scans of cadaveric specimens and the observed intra-class correlation coefficient was a high value of 0.98. Experimental results demonstrate the feasibility of application of FEM with high sensitivity and reproducibility on MDCT imaging of TB at distal tibia under in vivo condition.
| Identifer | oai:union.ndltd.org:uiowa.edu/oai:ir.uiowa.edu:etd-5481 |
| Date | 01 December 2014 |
| Creators | Chen, Cheng |
| Contributors | Saha, Punam K. |
| Publisher | University of Iowa |
| Source Sets | University of Iowa |
| Language | English |
| Detected Language | English |
| Type | thesis |
| Format | application/pdf |
| Source | Theses and Dissertations |
| Rights | Copyright 2014 Cheng Chen |
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