An Attention-Based Denoising Model for Diffusion Weighted Imaging

2026-06-02

AI Summary

This work addresses the challenge of signal-dependent Rician noise in accelerated diffusion-weighted imaging (DWI), which hinders effective denoising by conventional convolutional methods. To overcome this limitation, the authors propose a noise-aware, attention-driven denoising framework that innovatively integrates explicit noise-level conditional embeddings, hierarchical Swin Transformer window attention, and a Transformer-based multidimensional gated refinement mechanism. The architecture further incorporates residual learning and channel-adaptive attention to enable adaptive modeling of heteroscedastic noise. Evaluated across noise levels ranging from 1% to 15%, the method achieves an average PSNR of 33.69 dB and SSIM of 0.8539, demonstrating exceptional robustness and generalization even under severe noise conditions.

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.

Problem

• Diffusion-weighted imaging
• Denoising
• Rician noise
• Image quality
• Signal-dependent noise

Innovation

• attention mechanism
• Swin Transformer
• noise-aware denoising
• DWI restoration
• heteroscedastic noise