用注意力机制提升扩散成像去噪效果，适应不同噪声水平。
An Attention-Based Denoising Model for Diffusion Weighted Imaging
- 结合分层Swin注意力与多维门控精修，自适应建模噪声
- 在1%~15%噪声下实现33.69dB PSNR和0.8539 SSIM
- 适合医学影像去噪，尤其适用于低采样率扫描
扩散加权成像（DWI）用于全身癌症筛查，但通常需要较长的采集时间。当扫描时间缩短时，图像质量常因噪声增加而下降。DWI的幅度重建引入了依赖信号的Rician噪声，使传统卷积方法更难去噪。为此，我们提出一种噪声感知的注意力驱动去噪框架，结合分层Swin Transformer窗口注意力与基于Transformer的多维门控精修，用于DWI恢复。该模型引入显式的噪声水平条件输入和残差重建，实现对异方差噪声的自适应抑制，覆盖广泛的退化程度。在受污染的DWI扫描上进行实验评估，结果表明：在噪声水平1%~15%范围内，模型平均达到33.69 dB PSNR和0.8539 SSIM，且在严重噪声条件下表现稳定。这表明，注意力引导的上下文建模与通道自适应精修相结合，为DWI去噪提供了一种鲁棒且通用的解决方案。
原文摘要 · Abstract (English)
Diffusion-weighted imaging (DWI) is used for whole-body cancer screening, but it typically requires a long acquisition time. When the scan time is reduced, the image quality often suffers, leading to increased noise in the scans. Magnitude reconstruction in DWI introduces signal-dependent Rician noise, which makes denoising more challenging for conventional convolution-based methods. To address this limitation, we propose a noise-aware attention-driven denoising framework that integrates hierarchical Swin Transformer window attention with transformer-based multi-dimensional gated refinement for DWI restoration. The model incorporates explicit noise-level conditioning and residual reconstruction to enable adaptive suppression of heteroscedastic noise across a wide range of corruption levels. Experimental evaluation on corrupted DWI scans demonstrates strong restoration performance. Our model achieves a mean PSNR of 33.69~dB and SSIM of 0.8539 across noise levels from 1\% to 15\%, while maintaining stable behavior under severe noise conditions. These results indicate that attention-guided contextual modeling combined with channel-adaptive refinement provides a robust and generalizable solution for DWI denoising.
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