GCN-MIF: Graph Convolutional Network with Multi-Information Fusion for Low-dose CT Denoising

TL;DR

This paper introduces GCN-MIF, a novel graph convolutional network that explicitly fuses local, non-local, and contextual information for improved low-dose CT image denoising, outperforming existing methods.

Contribution

The paper presents a new GCN-based model that explicitly integrates multi-information for LDCT denoising, addressing limitations of implicit information utilization in prior CNN-based methods.

Findings

GCN-MIF outperforms existing denoising methods in quantitative metrics.

Visual results show clearer, less noisy CT images with preserved structures.

Clinical reader study confirms improved structural fidelity and noise suppression.

Abstract

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.