Title: ESFM-Net: low-dose CT artefact suppression method based on edge-guided spatial-frequency mutual network

Authors: Xueying Cui; Weisen Song; Xiaoling Han; Lizhong Jin; Hong Shangguan; Xiong Zhang

Addresses: School of Applied Sciences, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' School of Computer Sciences and Technology, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' School of Applied Sciences, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' School of Applied Sciences, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China ' School of Electronic Information Engineering, Taiyuan University of Science and Technology, Taiyuan, 030024, China

Abstract: Deep learning has shown superior performance in low-dose CT artefact suppression. However, the existing deep network has a limited perception of edge and texture information, and the transformer-based approaches have high computational complexity in calculating self-attention. To alleviate these issues, an artefact suppression method based on an edge-guided spatial-frequency mutual network (ESFM-Net) is proposed, which can enhance edge and texture information with low computational complexity. Specifically, an edge decoder is designed to supplement multi-scale edge details for reconstructing high-frequency in a single-encoder dual-decoder. For obtaining rich edge and texture information, a spatial-frequency feature extraction module is developed to obtain local spatial and global frequency features with little computational complexity. Considering the complementarity of information, a spatial-frequency mutual module is further constructed to enhance the feature representation capability by adaptive fusion. High and low-frequency features with different scales are also gradually fused through a multi-scale fusion module to obtain final denoised images. The comparative experiments and ablation results show the superior performance of our method in edges, texture preservation, and artefact suppression.

Keywords: low dose CT; artefact suppression; edge guidance; spatial-frequency mutual fusion.

DOI: 10.1504/IJSNET.2025.148197

International Journal of Sensor Networks, 2025 Vol.48 No.4, pp.241 - 254

Received: 21 Jan 2025
Accepted: 06 Feb 2025
Published online: 29 Aug 2025 *

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