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Low-dose computed tomography (LDCT) has attracted widespread attention in medical imaging due to its significant reduction in radiation exposure. However, compared to normal-dose CT (NDCT) images, LDCT images often contain considerable noise and artifacts, which severely affect diagnostic accuracy. In this paper, a novel dual-branch cross-attention transformer denoising network (DCANet) is proposed for LDCT denoising, which decouples and models the input images in different feature spaces through two complementary branching structures, and achieves feature complementarity and synergistic optimization through the fusion mechanism to improve overall characterization capability. The proposed DCANet includes Residual Triple Attention Blocks (RTAB) and a Cross-Attention Transformer module (CAformer), applied to the upper and lower branches respectively, effectively enabling the synergistic fusion of local detail enhancement and global structural modeling. Additionally, a joint optimization strategy using perceptual loss and Charbonnier loss is adopted, enabling the method to efficiently suppress noise while accurately preserving key structural and textural information in the images. Experimental results on the Mayo LDCT dataset demonstrate that the proposed DCANet achieves significant improvements in both quantitative metrics and perceptual quality.

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A Novel Dual-Branch Cross-Attention Transformer Network for Low-Dose CT Denoising

  • 会议论文

  • 发表日期：

    2026年

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  • Yuqin Li,
  • Mengcheng Huang,
  • Xu Wang,
  • Fei He,
  • Zhengang Jiang

摘要

Low-dose computed tomography (LDCT) has attracted widespread attention in medical imaging due to its significant reduction in radiation exposure. However, compared to normal-dose CT (NDCT) images, LDCT images often contain considerable noise and artifacts, which severely affect diagnostic accuracy. In this paper, a novel dual-branch cross-attention transformer denoising network (DCANet) is proposed for LDCT denoising, which decouples and models the input images in different feature spaces through two complementary branching structures, and achieves feature complementarity and synergistic optimization through the fusion mechanism to improve overall characterization capability. The proposed DCANet includes Residual Triple Attention Blocks (RTAB) and a Cross-Attention Transformer module (CAformer), applied to the upper and lower branches respectively, effectively enabling the synergistic fusion of local detail enhancement and global structural modeling. Additionally, a joint optimization strategy using perceptual loss and Charbonnier loss is adopted, enabling the method to efficiently suppress noise while accurately preserving key structural and textural information in the images. Experimental results on the Mayo LDCT dataset demonstrate that the proposed DCANet achieves significant improvements in both quantitative metrics and perceptual quality.