An Attention-Based Denoising Model for Diffusion Weighted Imaging
2026-06-02 • Computer Vision and Pattern Recognition
Computer Vision and Pattern Recognition
AI summaryⓘ
The authors address the problem of noisy images in diffusion-weighted imaging (DWI), which is important for cancer screening but often noisy when scan times are short. They developed a new method that uses advanced attention-based transformers to clean up these noisy images by understanding and adapting to different noise levels. Their approach improves image quality significantly, even under tough noise conditions. This could help make faster DWI scans more reliable for medical use.
Diffusion-weighted imagingDenoisingRician noiseSwin TransformerAttention mechanismNoise-level conditioningImage restorationMedical imagingPSNRSSIM
Authors
Prithviraj Verma, Pawan Kumar, Chandan Deshani, Prasun Chandra Tripathi
Abstract
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.