自适应核MPPCA提升高b值DWI图像去噪效果
Structure-Aware Adaptive Kernel MPPCA Denoising for Diffusion MRI
- 根据局部结构动态选择最优邻域块大小
- 在不同结构区域显著提升去噪性能
- 适合需要高精度扩散MRI的临床研究者
高b值扩散加权MRI（DWI）常因信噪比低导致图像质量差。马尔琴科-帕斯图尔主成分分析（MPPCA）是一种常用的去噪方法，但其在整个图像中使用固定邻域块大小，难以适应不同结构区域。为此，我们提出自适应核MPPCA（ak-MPPCA），根据每个体素的局部邻域特征自动选择最佳块大小，从而更好应对结构差异，提升去噪效果。
原文摘要 · Abstract (English)
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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