  Given the potential X-ray radiation risk to the patient, low-dose CT has
attracted a considerable interest in the medical imaging field. The current
main stream low-dose CT methods include vendor-specific sinogram domain
filtration and iterative reconstruction, but they need to access original raw
data whose formats are not transparent to most users. Due to the difficulty of
modeling the statistical characteristics in the image domain, the existing
methods for directly processing reconstructed images cannot eliminate image
noise very well while keeping structural details. Inspired by the idea of deep
learning, here we combine the autoencoder, the deconvolution network, and
shortcut connections into the residual encoder-decoder convolutional neural
network (RED-CNN) for low-dose CT imaging. After patch-based training, the
proposed RED-CNN achieves a competitive performance relative to
the-state-of-art methods in both simulated and clinical cases. Especially, our
method has been favorably evaluated in terms of noise suppression, structural
preservation and lesion detection.
