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DWI Denoising Using Spatial, Angular, and Radiometric Filtering

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Part of the Lecture Notes in Computer Science book series (LNIP,volume 7509)

Abstract

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.

Keywords

  • Gray Matter
  • Angular Component
  • Rician Noise
  • Voxel Location
  • Denoising Scheme

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2012 Springer-Verlag Berlin Heidelberg

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Yap, PT., Shen, D. (2012). DWI Denoising Using Spatial, Angular, and Radiometric Filtering. In: Yap, PT., Liu, T., Shen, D., Westin, CF., Shen, L. (eds) Multimodal Brain Image Analysis. MBIA 2012. Lecture Notes in Computer Science, vol 7509. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33530-3_17

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  • DOI: https://doi.org/10.1007/978-3-642-33530-3_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-33529-7

  • Online ISBN: 978-3-642-33530-3

  • eBook Packages: Computer ScienceComputer Science (R0)