High resolution peripheral quantitative computed tomography (HR-pQCT) is a promising method for detailed in vivo 3D characterization of the densitometric, geometric, and microstructural features of human bone. Currently, a hybrid densitometric, direct, and plate model-based calculation is used to quantify trabecular microstructure. In the present study, this legacy methodology is compared to direct methods derived from a new local thresholding scheme independent of densitometric and model assumptions.
Human femoral trabecular bone samples were acquired from patients undergoing hip replacement surgery. HR-pQCT (82 μm isotropic voxels) and micro-tomography (16 μm isotropic voxels) images were acquired. HR-pQCT images were segmented and analyzed in three ways: (1) using the hybrid method provided by the manufacturer based on a fixed global threshold, (2) using direct 3D methods based on the fixed global threshold segmentation, and (3) using direct 3D methods based on a novel local threshold scheme. The results were compared against standard direct 3D indices from μCT analysis.
Standard trabecular parameters determined by HR-pQCT correlated strongly to μCT. BV/TV and Tb.Th were significantly underestimated by the hybrid method and significantly overestimated by direct methods based on the global threshold segmentation while the local method yielded optimal intermediate results. The direct-local method also performed favorably for Tb.N (R2 = 0.85 vs. R2 = 0.70 for direct-global method) and Tb.Sp (R2 = 0.93 vs. R2 = 0.85 for the hybrid method and R2 = 0.87 for the direct-global method).
These results indicate that direct methods, with the aid of advanced segmentation techniques, may yield equivalent or improved accuracy for quantification of trabecular bone microstructure without relying on densitometric or model assumptions.
High resolution peripheral quantitative computed tomography Micro-tomography Trabecular bone Threshold Image processing
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The authors would like to acknowledge funding support from NIH RO1 AG17762 (SM). Furthermore, they would like to thank Dr. Andres Laib and Scanco Medical AG for providing software development consultation and for providing an API for IPL (Image Processing Language, Scanco Medical AG, Bassersdorf, Switzerland). They would also like to thank Dr. Michael Ries of the UCSF Department of Orthopaedic Surgery for providing the tissue used in this study.
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