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Frequency-Spatial Complementary Attention Network for Computed Tomography
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DOI：10.1016/j.knosys.2026.115468.png)
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摘要
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Computed tomography (CT) denoising is essential for clinical diagnosis and industrial inspection, but it is challenged by various noise and structural artifacts. Existing deep learning methods are limited by insufficient modeling of long-term dependencies, a disregard for intrinsic frequency-domain priors, and a significant domain gap caused by their reliance on unrealistic synthetic noise. To address these issues, a frequency-spatial complementary attention network (FSCANet) is proposed, which is based on the complementary fusion of frequency and spatial domains. The frequency domain branch explicitly decouples structural and phase information to model global context, while the spatial-domain branch improves local details. Simultaneously, a real-data-guided physics-informed noise model is introduced to bridge the domain gap by formalizing the physical noise generation process as a differentiable layer. FSCANet and the noise model are jointly optimized using a hybrid data-driven co-optimization strategy, resulting in a dynamic feedback loop that not only compels the noise model to generate physically interpretable noise but also drives FSCANet to achieve greater robustness. FSCANet achieves state-of-the-art performance on the DeepLesion dataset with a PSNR of 40.5861 dB and an SSIM of 0.9913, and demonstrates robust generalization on authentic clinical data from the Mayo dataset.
Keyword:
CT denoising
frequency-domain priors
spatial attention
physics-informed noise model
deep learning
期刊
K
IF:
7.6
论文数:
1.3W
被引数:
4.5W
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