  With substantial public concerns on potential cancer risks and health hazards
caused by the accumulated radiation exposure in medical imaging, reducing
radiation dose in X-ray based medical imaging such as Computed Tomography
Perfusion (CTP) has raised significant research interests. In this paper, we
embrace the deep Convolutional Neural Networks (CNN) based approaches and
introduce Smoothed Dense-Convolution Neural Network (SDCNet) to recover
high-dose quality CTP images from low-dose ones. SDCNet is composed of
sub-network blocks cascaded by skip-connections to infer the noise
(differentials) from paired low/high-dose CT scans. SDCNet can effectively
remove the noise in real low-dose CT scans and enhance the quality of medical
images. We evaluate the proposed architecture on thousands of CT perfusion
frames for both reconstructed image denoising and perfusion map quantification
including cerebral blood flow (CBF) and cerebral blood volume (CBV). SDCNet
achieves high performance in both visual and quantitative results with
promising computational efficiency, comparing favorably with state-of-the-art
approaches. \textit{The code is available at
\url{https://github.com/cswin/RC-Nets}}.
