{"ID":2873405,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.06591","arxiv_id":"2509.06591","title":"Hybrid Swin Attention Networks for Simultaneously Low-Dose PET and CT Denoising","abstract":"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.","short_abstract":"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 p...","url_abs":"https://arxiv.org/abs/2509.06591","url_pdf":"https://arxiv.org/pdf/2509.06591v7","authors":"[\"Yichao Liu\",\"Hengzhi Xue\",\"YueYang Teng\",\"Junwen Guo\"]","published":"2025-09-08T12:02:38Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false}