  Low dose X-ray computed tomography (LDCT) is desirable for reduced patient
dose. This work develops image reconstruction methods with deep learning (DL)
regularization for LDCT. Our methods are based on unrolling of proximal
forward-backward splitting (PFBS) framework with data-driven image
regularization via deep neural networks. In contrast with PFBS-IR that utilizes
standard data fidelity updates via iterative reconstruction (IR) method,
PFBS-AIR involves preconditioned data fidelity updates that fuse analytical
reconstruction (AR) method and IR in a synergistic way, I.e. fused analytical
and iterative reconstruction (AIR). The results suggest that DL-regularized
methods (PFBS-IR and PFBS-AIR) provided better reconstruction quality from
conventional wisdoms (AR or IR), and DL-based postprocessing method
(FBPConvNet). In addition, owing to AIR, PFBS-AIR noticeably outperformed
PFBS-IR.
