Spatially Adaptive DWI Denoising using Plug-and-Play Diffusion Models

Motivation: Diffusion-weighted imaging (DWI) is essential in clinical settings but often has a low and spatially variable signal-to-noise ratio (SNR), particularly in under-sampled high-b acquisitions of small organs. Goal(s): To accelerate DWI scans by reducing the number of image repetitions needed and overall acquisition time while maintaining image quality. Approach: Integrate a plug-and-play (PnP) method with a diffusion sampling framework that uses calculated noise map information to adaptively denoise accelerated DWI data affected by spatially varying noise. Results: Our method successfully preserves the content of images and prevents over-smoothing, even at high denoising settings. This efficiency enables a significant reduction in DWI scan times. Impact: We present a denoising method that accelerates DWI scans through a PnP diffusion model that utilizes noise maps for guidance. This approach improves scanning efficiency while preserving image quality, showcasing promise for future DWI clinical applications.