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HR-pQCT enables in vivo multi-parametric assessments of bone microstructure in the distal radius and distal tibia. Conventional HR-pQCT image analysis approaches summarize bone parameters into global scalars, discarding relevant spatial information. In this work, we demonstrate the feasibility and reliability of statistical parametric mapping (SPM) techniques for HR-pQCT studies, which enable population-based local comparisons of bone properties. We present voxel-based morphometry (VBM) to assess trabecular and cortical bone voxel-based features, and a surface-based framework to assess cortical bone features both in cross-sectional and longitudinal studies. In addition, we present tensor-based morphometry (TBM) to assess trabecular and cortical bone structural changes. The SPM techniques were evaluated based on scan-rescan HR-pQCT acquisitions with repositioning of the distal radius and distal tibia of 30 subjects. For VBM and surface-based SPM purposes, all scans were spatially normalized to common radial and tibial templates, while for TBM purposes, rescans (follow-up) were spatially normalized to their corresponding scans (baseline). VBM was evaluated based on maps of local bone volume fraction (BV/TV), homogenized volumetric bone mineral density (vBMD), and homogenized strain energy density (SED) derived from micro-finite element analysis; while the cortical bone framework was evaluated based on surface maps of cortical bone thickness, vBMD, and SED. Voxel-wise and vertex-wise comparisons of bone features were done between the groups of baseline and follow-up scans. TBM was evaluated based on mean square errors of determinants of Jacobians at baseline bone voxels. In both anatomical sites, voxel- and vertex-wise uni- and multi-parametric comparisons yielded non-significant differences, and TBM showed no artefactual bone loss or apposition. The presented SPM techniques demonstrated robust specificity thus warranting their application in future clinical HR-pQCT studies.

Statistical Parametric Mapping of HR-pQCT Images: A Tool for Population-Based Local Comparisons of Micro-Scale Bone Features