DWI Denoising Using Spatial, Angular, and Radiometric Filtering
In this paper, we study the effectiveness of the concurrent utilization of spatial, angular, and radiometric (SAR) information for denoising diffusion-weighted data. SAR filtering smooths diffusion-weighted images while at the same time preserves edges by means of nonlinear combination of nearby and similar signal values. The method is noniterative, local, and simple. It combines diffusion signals based on both their spatio-angular closeness and their radiometric similarity, with greater preference given to nearby and similar values. Our results suggest that SAR filtering reveals structures that are concealed by noise and produces anisotropy maps with markedly improved quality.
KeywordsGray Matter Angular Component Rician Noise Voxel Location Denoising Scheme
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