DAN-Net: Dual-Domain Adaptive-Scaling Non-local Network for CT Metal Artifact Reduction

Abstract

Metal implants can heavily attenuate X-rays in computed tomography (CT) scans, leading to severe artifacts in reconstructed images, which significantly jeopardize image quality and negatively impact subsequent diagnoses and treatment planning. With the rapid development of deep learning in the field of medical imaging, several network models have been proposed for metal artifact reduction (MAR) in CT. Despite the encouraging results achieved by these methods, there is still much room to further improve performance. In this paper, a novel Dual-domain Adaptive-scaling Non-local network (DAN-Net) for MAR. We correct the corrupted sinogram using adaptive scaling first to preserve more tissue and bone details as a more informative input. Then, an end-to-end dual-domain network is adopted to successively process the sinogram and its corresponding reconstructed image generated by the analytical reconstruction layer. In addition, to better suppress the existing artifacts and restrain the potential secondary artifacts caused by inaccurate results of the sinogram-domain network, a novel residual sinogram learning strategy and nonlocal module are leveraged in the proposed network model. In the experiments, the proposed DAN-Net demonstrates performance competitive with several state-of-the-art MAR methods in both qualitative and quantitative aspects.

arXiv comment29 pages, 11 figures

References in corpus (9)

• PyTorch: An Imperative Style, High-Performance Deep Learning Library
• Fully Convolutional Networks for Semantic Segmentation
• Deep Convolutional Neural Network for Inverse Problems in Imaging
• Convolutional Neural Network Based Metal Artifact Reduction in X-ray Computed Tomography
• ADN: Artifact Disentanglement Network for Unsupervised Metal Artifact Reduction
• CT sinogram-consistency learning for metal-induced beam hardening correction
• Deep Learning Methods for Solving Linear Inverse Problems: Research Directions and Paradigms
• Deep Lesion Graphs in the Wild: Relationship Learning and Organization of Significant Radiology Image Findings in a Diverse Large-scale Lesion Database
• Generative Mask Pyramid Network for CT/CBCT Metal Artifact Reduction with Joint Projection-Sinogram Correction

Cited by in corpus (3)

• Metal Artifact Reduction in 2D CT Images with Self-supervised Cross-domain Learning
• PDS-MAR: a fine-grained Projection-Domain Segmentation-based Metal Artifact Reduction method for intraoperative CBCT images with guidewires
• Small metal artifact detection and inpainting in cardiac CT images