  Being low-level radiation exposure and less harmful to health, low-dose
computed tomography (LDCT) has been widely adopted in the early screening of
lung cancer and COVID-19. LDCT images inevitably suffer from the degradation
problem caused by complex noises. It was reported that deep learning (DL)-based
LDCT denoising methods using convolutional neural network (CNN) achieved
impressive denoising performance. Although most existing DL-based methods
(e.g., encoder-decoder framework) can implicitly utilize non-local and
contextual information via downsampling operator and 3D CNN, the explicit
multi-information (i.e., local, non-local, and contextual) integration may not
be explored enough. To address this issue, we propose a novel graph
convolutional network-based LDCT denoising model, namely GCN-MIF, to explicitly
perform multi-information fusion for denoising purpose. Concretely, by
constructing intra- and inter-slice graph, the graph convolutional network is
introduced to leverage the non-local and contextual relationships among pixels.
The traditional CNN is adopted for the extraction of local information.
Finally, the proposed GCN-MIF model fuses all the extracted local, non-local,
and contextual information. Extensive experiments show the effectiveness of our
proposed GCN-MIF model by quantitative and visualized results. Furthermore, a
double-blind reader study on a public clinical dataset is also performed to
validate the usability of denoising results in terms of the structural
fidelity, the noise suppression, and the overall score. Models and code are
available at https://github.com/tonyckc/GCN-MIF_demo.
