  Obtaining accurate and reliable images from low-dose computed tomography (CT)
is challenging. Regression convolutional neural network (CNN) models that are
learned from training data are increasingly gaining attention in low-dose CT
reconstruction. This paper modifies the architecture of an iterative regression
CNN, BCD-Net, for fast, stable, and accurate low-dose CT reconstruction, and
presents the convergence property of the modified BCD-Net. Numerical results
with phantom data show that applying faster numerical solvers to model-based
image reconstruction (MBIR) modules of BCD-Net leads to faster and more
accurate BCD-Net; BCD-Net significantly improves the reconstruction accuracy,
compared to the state-of-the-art MBIR method using learned transforms; BCD-Net
achieves better image quality, compared to a state-of-the-art iterative NN
architecture, ADMM-Net. Numerical results with clinical data show that BCD-Net
generalizes significantly better than a state-of-the-art deep (non-iterative)
regression NN, FBPConvNet, that lacks MBIR modules.
