  Deep neural networks have a great potential to improve image denoising in
low-dose computed tomography (LDCT). Popular ways to increase the network
capacity include adding more layers or repeating a modularized clone model in a
sequence. In such sequential architectures, the noisy input image and end
output image are commonly used only once in the training model, which however
limits the overall learning performance. In this paper, we propose a
parallel-clone neural network method that utilizes a modularized network model
and exploits the benefit of parallel input, parallel-output loss, and
clone-toclone feature transfer. The proposed model keeps a similar or less
number of unknown network weights as compared to conventional models but can
accelerate the learning process significantly. The method was evaluated using
the Mayo LDCT dataset and compared with existing deep learning models. The
results show that the use of parallel input, parallel-output loss, and
clone-to-clone feature transfer all can contribute to an accelerated
convergence of deep learning and lead to improved image quality in testing. The
parallel-clone network has been demonstrated promising for LDCT image
denoising.
