An LDCT image denoising model based on dual-path attention

Abstract

Existing denoising methods for low-dose computed tomography (LDCT) images generally suffer from an imbalance between global correlation capture and local detail preservation, leading to blurring of the global image structure or loss of edge textures, which seriously impairs the accuracy of clinical diagnosis. To address this limitation, a dual-path attention denoising model is proposed. An LSTM-based attention module (LBAM) is introduced: it captures long-range sequence dependencies through a gating mechanism, focuses on key diagnostic information using residual connections, and enhances the modeling of cross-regional continuous morphology in the sequence dimension. Meanwhile, a multi-scale attention module (MSAM) is embedded in the U-Net architecture, which performs parallel modeling via channel-spatial attention and self-attention to achieve collaborative optimization of local detail enhancement and global correlation capture. The results show that the method can effectively balance the integrity of the global structure and the clarity of local details, significantly improving the quality of LDCT images. The PSNR and SSIM values in the Mayo dataset were 31.6725 and 0.8820, respectively, indicating the effectiveness of the method in removing noise and preserving image details.