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Osteoporosis is a skeletal disorder that leads to increased fracture risk dueto decreased strength of cortical and trabecular bone. Even withstate-of-the-art non-invasive assessment methods there is still a highunderdiagnosis rate. Quantitative computed tomography (QCT) permits theselective analysis of cortical bone, however the low spatial resolution ofclinical QCT leads to an overestimation of the thickness of cortical bone(Ct.Th) and bone strength.  We propose a novel, model based, fully automatic image analysis method thatallows accurate spatial modeling of the thickness distribution of cortical bonefrom clinical QCT. In an analysis-by-synthesis (AbS) fashion a stochastic scanis synthesized from a probabilistic bone model, the optimal model parametersare estimated using a maximum a-posteriori approach. By exploiting thedifferent characteristics of in-plane and out-of-plane point spread functionsof CT scanners the proposed method is able assess the spatial distribution ofcortical thickness.  The method was evaluated on eleven cadaveric human vertebrae, scanned byclinical QCT and analyzed using standard methods and AbS, both compared to highresolution peripheral QCT (HR-pQCT) as gold standard. While standard QCT basedmeasurements overestimated Ct.Th. by 560% and did not show significantcorrelation with the gold standard ($r^2 = 0.20,\, p = 0.169$) the proposedmethod eliminated the overestimation and showed a significant tight correlationwith the gold standard ($r^2 = 0.98,\, p < 0.0001$) a root mean square errorbelow 10%.

An Analysis by Synthesis Method that Allows Accurate Spatial Modeling of Thickness of Cortical Bone from Clinical QCT