  We present a novel reconstruction algorithm based on a general cone-beam CT
forward model which is capable of incorporating the blur and noise correlations
that are exhibited in flat-panel CBCT measurement data. Specifically, the
proposed model may include scintillator blur, focal-spot blur, and noise
correlations due to light spread in the scintillator. The proposed algorithm
(GPL-BC) uses a Gaussian Penalized-Likelihood objective function which
incorporates models of Blur and Correlated noise. In a simulation study, GPL-BC
was able to achieve lower bias as compared to deblurring followed by FDK as
well as a model-based reconstruction method without integration of measurement
blur. In the same study, GPL-BC was able to achieve better line-pair
reconstructions (in terms of segmented-image accuracy) as compared to
deblurring followed by FDK, a model based method without blur, and a model
based method with blur but not noise correlations. A prototype extremities
quantitative cone-beam CT test bench was used to image a physical sample of
human trabecular bone. These data were used to compare reconstructions using
the proposed method and model based methods without blur and/or correlation to
a registered {\mu}CT image of the same bone sample. The GPL-BC reconstructions
resulted in more accurate trabecular bone segmentation. Multiple trabecular
bone metrics, including Trabecular Thickness (Tb.Th.) were computed for each
reconstruction approach as well as the {\mu}CT volume. The GPL-BC
reconstruction provided the most accurate Tb.Th. measurement, 0.255 mm, as
compared to the {\mu}CT derived value of 0.193 mm, followed by the GPL-B
reconstruction, the GPL-I reconstruction, and then the FDK reconstruction
(0.271 mm, 0.309 mm, and 0.335 mm, respectively).
