Unsupervised denoising of prostate DWI

The diagnostic value of diffusion-weighted MR images is often degraded by their inherently low signal-to-noise ratio (SNR), especially for high b-values. In this context, the application of learning-based denoising methods is difficult since most methods require noise-free target images for training. We show how to denoise and evaluate diffusion-weighted MR images in a self-supervised manner by exploiting an adapted version of Stein’s unbiased risk estimator and specific properties of the data. Both quantitative and qualitative evaluations indicate increased performance over state-of-the-art unsupervised denoising methods.