Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866918231323705344 |
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| author | Singhal, Ananya Shanbhag, Dattesh Dayanand Chatterjee, Sudhanya |
| author_facet | Singhal, Ananya Shanbhag, Dattesh Dayanand Chatterjee, Sudhanya |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_04586 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI Singhal, Ananya Shanbhag, Dattesh Dayanand Chatterjee, Sudhanya Image and Video Processing 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. |
| title | Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2512.04586 |