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.
Mostapha et al. (Tue,) studied this question.