  While model-based reconstruction methods have been successfully applied to
flat-panel cone-beam CT (FP-CBCT) systems, typical implementations ignore both
spatial correlations in the projection data as well as system blurs due to the
detector and focal spot in the x-ray source. In this work, we develop a forward
model for flat-panel-based systems that includes blur and noise correlation
associated with finite focal spot size and an indirect detector (e.g.,
scintillator). This forward model is used to develop a staged reconstruction
framework where projection data are deconvolved and log-transformed, followed
by a generalized least-squares reconstruction that utilizes a non-diagonal
statistical weighting to account for the correlation that arises from the
acquisition and data processing chain. We investigate the performance of this
novel reconstruction approach in both simulated data and in CBCT test-bench
data. In comparison to traditional filtered backprojection and model-based
methods that ignore noise correlation, the proposed approach yields a superior
noise-resolution tradeoff. For example, for a system with 0.34 mm FWHM
scintillator blur and 0.70 FWHM focal spot blur, using the a correlated noise
model instead of an uncorrelated noise model increased resolution by 42% (with
variance matched at 6.9 x 10^-8 mm^-2 ). While this advantage holds across a
wide range of systems with differing blur characteristics, the improvements are
greatest for systems where source blur is larger than detector blur.
