Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI

2025/12/04 by Singhal, Ananya, Shanbhag, Dattesh Dayanand, Chatterjee, Sudhanya
Medicine · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #FOS: Electrical engineering #Image and Video Processing (eess.IV) #MRI in cancer diagnosis #electronic engineering #information engineering

paper · doi:10.48550/arxiv.2512.04586

openalex publication_date 2025/12/04 · openalex created_date 2025/12/06 · openalex updated_date 2026/07/28

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.

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