  Osteoporosis is a skeletal disorder that leads to increased fracture risk due
to decreased strength of cortical and trabecular bone. Even with
state-of-the-art non-invasive assessment methods there is still a high
underdiagnosis rate. Quantitative computed tomography (QCT) permits the
selective analysis of cortical bone, however the low spatial resolution of
clinical 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 that
allows accurate spatial modeling of the thickness distribution of cortical bone
from clinical QCT. In an analysis-by-synthesis (AbS) fashion a stochastic scan
is synthesized from a probabilistic bone model, the optimal model parameters
are estimated using a maximum a-posteriori approach. By exploiting the
different characteristics of in-plane and out-of-plane point spread functions
of CT scanners the proposed method is able assess the spatial distribution of
cortical thickness.
  The method was evaluated on eleven cadaveric human vertebrae, scanned by
clinical QCT and analyzed using standard methods and AbS, both compared to high
resolution peripheral QCT (HR-pQCT) as gold standard. While standard QCT based
measurements overestimated Ct.Th. by 560% and did not show significant
correlation with the gold standard ($r^2 = 0.20,\, p = 0.169$) the proposed
method eliminated the overestimation and showed a significant tight correlation
with the gold standard ($r^2 = 0.98,\, p < 0.0001$) a root mean square error
below 10%.
