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https://hdl.handle.net/10356/50070
Title: | Quantification of trabecular bone structure using magnetic resonance imaging. | Authors: | Lim, Jun Hong. | Keywords: | DRNTU::Engineering::Bioengineering | Issue Date: | 2012 | Abstract: | The purpose of this study is to evaluate the potential of magnetic resonance imaging (MRI) as a diagnostic tool for osteoporosis and to evaluate image processing techniques for improvement of characterization of trabecular bone. Twenty bone cylinders obtained from goat’s femoral head were scanned with microcomputed tomography (μCT) and 3 Tesla MRI machine. μCT was used as the standard of reference in this study. MR images were processed by image contrast enhancement, super resolution volume reconstruction and both. Trabecular bones were extracted from MR Images by K-means clustering. Four histomorphometric parameters namely bone volume fraction (BVF), trabecular thickness (Tb.Th), trabecular number (Tb.N) and trabecular spacing (Tb.Sp) were derived. Bone mineral density (BMD) was also measured by dual energy X-ray absorptiometry. Correlation studies were done using linear regression analysis. The MRI correlations of parameters were moderate with R2= 0.534 for BVF, R2= 0.254 for Tb.Th, R2= 0. 223 for Tb.N and R2= 0. 237 for Tb.Sp. Correlations were significantly improved after super resolution volume reconstruction with R2= 0.651 for BVF, R2= 0.413 for Tb.Th, R2= 0.317 for Tb.N and R2= 0.402 for Tb.Sp. Improvements to R2 were also found in the correlations with BMD. The results of this study demonstrated that image processing can improve MRI derived parameters correlation with μCT. Thus, MRI has the ability to quantify trabecular bone structures and can be use as a tool to predict osteoporosis. | URI: | http://hdl.handle.net/10356/50070 | Schools: | School of Chemical and Biomedical Engineering | Rights: | Nanyang Technological University | Fulltext Permission: | restricted | Fulltext Availability: | With Fulltext |
Appears in Collections: | SCBE Student Reports (FYP/IA/PA/PI) |
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SCBE078.pdf Restricted Access | Report | 1.6 MB | Adobe PDF | View/Open |
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