  Low-dose computed tomography (CT) has attracted a major attention in the
medical imaging field, since CT-associated x-ray radiation carries health risks
for patients. The reduction of CT radiation dose, however, compromises the
signal-to-noise ratio, and may compromise the image quality and the diagnostic
performance. Recently, deep-learning-based algorithms have achieved promising
results in low-dose CT denoising, especially convolutional neural network (CNN)
and generative adversarial network (GAN). This article introduces a Contracting
Path-based Convolutional Encoder-decoder (CPCE) network in 2D and 3D
configurations within the GAN framework for low-dose CT denoising. A novel
feature of our approach is that an initial 3D CPCE denoising model can be
directly obtained by extending a trained 2D CNN and then fine-tuned to
incorporate 3D spatial information from adjacent slices. Based on the transfer
learning from 2D to 3D, the 3D network converges faster and achieves a better
denoising performance than that trained from scratch. By comparing the CPCE
with recently published methods based on the simulated Mayo dataset and the
real MGH dataset, we demonstrate that the 3D CPCE denoising model has a better
performance, suppressing image noise and preserving subtle structures.
