针对当前低剂量计算机断层扫描（LDCT）重建图像中存在复杂噪声与条纹伪影的问题，提出了一种基于双注意力机制和复合损失的LDCT去噪方法。该方法通过引入空间注意力机制与通道注意力机制，获取了全局特征信息，并对特征权重进行重标定，使重要的结构细节能够得以保留，从而提升网络的去噪性能；同时加入感知损失度量函数，使对人眼而言敏感的纹理信息得到保留。实验结果表明：在视觉效果上，所提方法不仅去除了LDCT图像中的噪声和伪影，同时也保留了更多的纹理特征与结构细节；峰值信噪比（PSNR）等客观指标均高于其他对比方法。

Aiming at the problem of complex noise and fringe artifacts in current low-dose computed tomography (LDCT) reconstructed images, a LDCT denoising method based on dual attention mechanism and compound loss is proposed. This method obtains global feature information by introducing spatial attention mechanism and channel attention mechanism, and recalibrates the feature weights, so that important structural details can be retained, thereby improving the denoising performance of the network; at the same time, the perceptual loss measurement function is added to preserve the texture information sensitive to human eyes. Experimental results show that, in terms of visual effects, the proposed algorithm not only removes noise and artifacts in LDCT images, but also retains more texture features and structural details; objective indicators such as peak signal-to-noise ratio (PSNR) are are higher than that of other comparison methods.