Generalized Penalized Weighted Least-Squares Reconstruction for Deblurred Flat-Panel CBCT

An increase in achievable spatial resolution would enable application of flat-panel detector cone-beam CT (FPCBCT) in high-resolution applications, such as 3D breast and bone imaging. Improved reconstruction techniques exploiting more sophisticated models of noise and spatial resolution characteristics may provide such improvement. In this work, we propose a statistical model of measurement mean and covariance, with particular attention given to correlations in measurement data. An iterative reconstruction algorithm based on this model was applied to simulated and real experimental data to evaluate its performance compared to an uncorrelated model under various imaging conditions. Focal spot and detector blur of a CBCT testbench were measured and used to inform the model-based image reconstruction. Simulation studies demonstrate that incorporation of the correlated noise model yields improved reconstructions, especially in the presence of focal spot blur. Improved noise-resolution tradeoffs were quantified, and application to high-resolution imaging of trabecular bone was