Low-dose computed tomography (LDCT) and positron emission tomography (PET) have emerged as safer alternatives to conventional imaging modalities by significantly reducing radiation exposure. However, current approaches often face a trade$-$off between training stability and computational efficiency. In this study, we propose a novel Hybrid Swin Attention Network (HSANet), which incorporates Efficient Global Attention (EGA) modules and a hybrid upsampling module to address these limitations. The EGA modules enhance both spatial and channel-wise interaction, improving the network's capacity to capture relevant features, while the hybrid upsampling module mitigates the risk of overfitting to noise. We validate the proposed approach using a publicly available LDCT/PET dataset. Experimental results demonstrate that HSANet achieves superior denoising performance compared to state of the art methods, while maintaining a lightweight model size suitable for deployment on GPUs with standard memory configurations. Thus, our approach demonstrates significant potential for practical, real-world clinical applications.

翻译：低剂量计算机断层扫描（LDCT）与正电子发射断层扫描（PET）通过显著降低辐射剂量，已成为传统成像模式更安全的替代方案。然而，现有方法通常在训练稳定性与计算效率之间存在权衡。本研究提出一种新颖的混合Swin注意力网络（HSANet），该网络结合了高效全局注意力（EGA）模块与混合上采样模块以应对这些局限。EGA模块增强了空间与通道维度的交互，提升了网络捕获相关特征的能力，而混合上采样模块则降低了模型对噪声过拟合的风险。我们在公开可用的LDCT/PET数据集上验证了所提方法。实验结果表明，与现有先进方法相比，HSANet实现了更优的去噪性能，同时保持了轻量化的模型规模，适合在标准内存配置的GPU上部署。因此，我们的方法展现出在实际临床应用中重要的实用潜力。