{"ID":2832897,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.04586","arxiv_id":"2512.04586","title":"Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI","abstract":"Diffusion-weighted MRI (DWI) at high b-values often suffers from low signal-to-noise ratio (SNR), making image quality poor. Marchenko-Pastur PCA (MPPCA) is a popular method to reduce noise, but it uses a fixed patch size across the whole image, which doesn't work well in regions with different structures. To address this, we propose an adaptive kernel MPPCA (ak-MPPCA) that selects the best patch size for each voxel based on its local neighborhood. This improves denoising performance by better handling structural variations.","short_abstract":"Diffusion-weighted MRI (DWI) at high b-values often suffers from low signal-to-noise ratio (SNR), making image quality poor. Marchenko-Pastur PCA (MPPCA) is a popular method to reduce noise, but it uses a fixed patch size across the whole image, which doesn't work well in regions with different structures. To address t...","url_abs":"https://arxiv.org/abs/2512.04586","url_pdf":"https://arxiv.org/pdf/2512.04586v1","authors":"[\"Ananya Singhal\",\"Dattesh Dayanand Shanbhag\",\"Sudhanya Chatterjee\"]","published":"2025-12-04T09:00:29Z","proceeding":"eess.IV","tasks":"[\"eess.IV\"]","methods":"[\"Diffusion Model\"]","has_code":false}