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Abstract #1160

Denoising Diffusion MRI with Self-supervised Learning on Coresets via Matrix Sketching

Shreyas Fadnavis1, Agniva Chowdhury2, Petros Drineas2, and Eleftherios Garyfallidis1
1Indiana University Bloomington, Bloomington, IN, United States, 2Purdue University, West Lafayette, IN, United States


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.

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