Concerns over the risks of radiation dose from diagnostic CT motivated the utilization of low dose CT (LdCT).However, due to the extremely low X-ray photon statistics in LdCT, the reconstruction problem is ill-posed and noisecontaminated.Conventional Compressed Sensing (CS) methods have been investigated to enhance the signal-to-noiseratio of LdCT at the cost of image resolution and low contrast object visibility. In this work, we adapted a flexible,iterative reconstruction framework, termed Plug-and-Play (PnP) alternating direction method of multipliers (ADMM),that incorporated state-of-the-art denoising algorithms into model-based image reconstruction. The PnP ADMMframework is achieved by combining a least square data fidelity term with a regularization term for image smoothnessand was solved through the ADMM. An off-the-shelf image denoiser, the Block-Matching 3D-transform shrinkage(BM3D) filter, is plugged in to substitute an ADMM module. The PnP ADMM was evaluated on low dose scans of ACR464 phantom and two lung screening data sets and is compared with the Filtered Back Projection (FBP), the TotalVariation (TV), the BM3D post-processing method, and the BM3D regularization method. The proposed frameworkdistinguished the line pairs at 9 lp/cm resolution on the ACR phantom and the fissure line in the left lung, resolving thesame or better image details than FBP reconstruction of higher dose scans with up to 18 times less dose. Compared withconventional iterative reconstruction methods resulting in comparable image noise, the proposed method is significantlybetter at recovering image details and improving low contrast conspicuity.
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