Iterative CT Reconstruction using Models of Source and Detector Blur and Correlated Noise

Statistical model-based reconstruction methods derive much of their advantage over traditional methods through more accurate forward models of the imaging system. Typical forward models fail to integrate two important aspects of real imaging systems: system blur and noise correlations in the measurements. This work develops an approach that models both aspects using a two-stage approach that includes a regularization deblurring operation followed by generalized penalized weighted least-squares reconstruction. Different reconstruction noise models including standard uncorrelated and correlated presumptions were explored. Moreover, different imaging systems were investigated in which blur was dominated by source effects, dominated by detector effects, or by a combination of source and detector blur. The proposed reconstruction approach that models the correlated noise demonstrated the best performance across all scenarios with the greatest benefits for increased source blur and for reconstructions with finer spatial resolution. This suggests potential application of the method for high resolution systems like dedicated flat-panel cone-beam CT (e.g., head, extremity, dental, mammography scanners) where system resolution is limited by both source and detector blur effects and noise correlations in measurement data are traditionally ignored.