A local adaptive threshold strategy for high resolution peripheral quantitative computed tomography of trabecular bone.
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 microm isotropic voxels) and micro-tomography (16 microm 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 microCT analysis. Standard trabecular parameters determined by HR-pQCT correlated strongly to microCT. 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 (R(2) = 0.85 vs. R(2) = 0.70 for direct-global method) and Tb.Sp (R(2) = 0.93 vs. R(2) = 0.85 for the hybrid method and R(2) = 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.
Burghardt
Andrew J
AJ
Musculoskeletal Quantitative Imaging Research Group, Department of Radiology, University of California, San Francisco, CA 94158, USA. andrew.burghardt@radiology.ucsf.edu
Kazakia
Galateia J
GJ
Majumdar
Sharmila
S
D015502:Absorptiometry, Photon / Q000379:methods*; D000465:Algorithms; D015519:Bone Density / Q000502:physiology*; D005269:Femur / Q000000981:diagnostic imaging* / Q000502:physiology*; D006801:Humans; D066298:In Vitro Techniques; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed / Q000379:methods*