  Lowering the radiation dose in computed tomography (CT) can greatly reduce
the potential risk to public health. However, the reconstructed images from the
dose-reduced CT or low-dose CT (LDCT) suffer from severe noise, compromising
the subsequent diagnosis and analysis. Recently, convolutional neural networks
have achieved promising results in removing noise from LDCT images; the network
architectures used are either handcrafted or built on top of conventional
networks such as ResNet and U-Net. Recent advance on neural network
architecture search (NAS) has proved that the network architecture has a
dramatic effect on the model performance, which indicates that current network
architectures for LDCT may be sub-optimal. Therefore, in this paper, we make
the first attempt to apply NAS to LDCT and propose a multi-scale and
multi-level NAS for LDCT denoising, termed MANAS. On the one hand, the proposed
MANAS fuses features extracted by different scale cells to capture multi-scale
image structural details. On the other hand, the proposed MANAS can search a
hybrid cell- and network-level structure for better performance. Extensively
experimental results on three different dose levels demonstrate that the
proposed MANAS can achieve better performance in terms of preserving image
structural details than several state-of-the-art methods. In addition, we also
validate the effectiveness of the multi-scale and multi-level architecture for
LDCT denoising.
