Structural analysis of cortical porosity applied to HR-pQCT data

Purpose: The investigation of cortical porosity is an important aspect of understanding biological, pathoetiological, and biomechanical processes occurring within the skeleton. With the emergence of HR-pQCT as a noninvasive tool suitable for clinical use, cortical porosity at appendicular sites can be directly visualized in vivo. The aim of this study was to introduce a novel topological analysis of the cortical pore network for HR-pQCT data and determine the influence of resolution on measures of cortical pore network microstructure and topology. Methods: Cadaveric radii were scanned using HR-pQCT at two different voxel sizes (41 and 82 mu m) and also using mu CT at a voxel size of 18 mu m. HR-pQCT and mu CT image sets were spatially coregistered. Segmentation and quantification of cortical porosity (Ct.Po) and mean pore diameter (Ct.Po.Dm) were achieved using an established extended cortical analysis technique. Topological classification of individual pores was performed using topology-preserving skeletonization and multicolor dilation algorithms. Based on the pore skeleton topological classification, the following parameters were quantified: total number of planar surface-skeleton canals (N.Slabs), tubular curve-skeleton canals (N.Tubes), and junction elements (N.Junctions), mean slab volume (Slab.Vol), mean tube volume (Tube.Vol), mean slab orientation (Slab.theta), mean tube orientation (Tube.theta), N.Slabs/N.Tubes, and integral (total) slab volume/integral tube volume (iSlab.Vol/iTube.Vol). An in vivo reproducibility study was also conducted to assess short-term precision of the topology parameters. Precision error was characterized using root mean square coefficient of variation (RMSCV%). Results: Correlations to mu CT values for Ct.Po were significant for both the 41 and 82 mu m HR-pQCT data (41: r(2) = 0.82, p < 0.001, 82: r(2) = 0.75, p < 0.001). For Ct.Po.Dm, only the 41 mu m data were significantly predictive of mu CT values (r(2) = 0.72, p < 0.01) Data at both HR-pQCT voxel sizes were strongly predictive of the mu CT values for N. Slabs (41: r(2) = 0.93, p < 0.001; 82: r(2) = 0.84, p < 0.001), N.Tubes (41: r(2) = 0.94, p < 0.001; 82: r(2) = 0.84, p < 0.001), and N. Junctions (41: r(2) = 0.93, p < 0.001; 82: r(2) = 0.78, p < 0.001), though proportional bias was evident in these correlations. Weak correlations were seen for iSlab.Vol/iTube.Vol at both voxel sizes (41: r(2) = 0.52, p < 0.01; 82: r(2) = 0.39, p < 0.05). Slab.Vol was significantly correlated to mu CT data at 41 mu m (r(2) = 0.60, p < 0.01) but not at 82 mu m, while Tube.Vol was significantly correlated at both voxel sizes (41: r(2) = 0.79, p < 0.001; 82: r(2) = 0.68, p < 0.01). In vivo precision error for these parameters ranged from 2.31 to 9.68 RMSCV%. Conclusions: Strong correlations between mu CT-and HR-pQCT-derived measurements were found, particularly in HR-pQCT images obtained at 41 mu m. These data are in agreement with our previous study investigating the effect of voxel size on standard HR-pQCT metrics of trabecular and cortical microstructure, and extend our previous findings to include topological descriptors of the cortical pore network. (C) 2014 American Association of Physicists in Medicine.