Diffusion MRI typically has a low SNR on account of the noise from a variety of sources corrupting the data. The state-of-the-art denoiser Patch2Self proposed a self-supervised learning technique that uses patches from all the voxels to learn the denoising function which in practice can be resource-intensive. We, therefore, propose Patch2Self2 which performs self-supervised denoising using coresets constructed via matrix sketching, resulting in significant speedups and reduced memory usage. Our results showed that sampling-based sketching via leverage scores gave the best performance. Remarkably, leverage scores can be directly used as a statistic for interpreting influential regions hampering the denoising performance.
This abstract and the presentation materials are available to members only; a login is required.