Iterative reconstruction of cone-beam breast CT using plug-and-play projected gradient descent

Abstract

Concerns on the risks of radiation dose in the cone-beam breast CT (CBBCT) motivated the development of low dose CBBCT (LdCBBCT). Due to the noisy and inadequate data acquisition in LdCBBCT, the conventional analytical Filtered Back Projection (FBP) algorithm tends to result in severe image artifacts and overwhelming noise. Model-based iterative reconstruction methods managed to reduce artifacts and enhance the signal-to-noise ratio but were unable to recover many fine structures and low contrast objects pertinent to diagnosis and treatment. To maintain the strengths of the model-based optimization framework and overcome its limitations in signal recovery, we adapted a CNN-based iterative reconstruction framework, termed Plugand-Play (PnP) proximal gradient descent (PGD) framework, that incorporated state-of-the-art deep-learningbased denoising algorithms with model-based image reconstruction. The PnP-PGD framework is achieved by combining a least-square data fidelity term for data consistency with a non-local regularization for image smoothness, which was solved via PGD. A deep convolutional neural network (DCNN) was plugged in to substitute the proximal operator of the regularization term. The PnP-PGD was evaluated on LdCBBCT scans of a breast phantom and was compared with Filtered Back Projection (FBP), Total Variation (TV), the BlockMatching 3D-transform shrinkage (BM3D), and the DCNN based post-processing method. Compared with FBP, iterative reconstruction, and BM3D, the proposed PnP-PGD substantially reduced image noise and artifacts. Compared with the DCNN based post-processing method, the PnP-PGD improved image contrast-tonoise ratio (CNR). The proposed PnP-PGD takes advantage of both model-based reconstruction and deeplearning-based denoisers, showing improved image quality.