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A fast algorithm for denoising magnitude diffusion-weighted images with rank and edge constraints.

Authors
  • Lam, Fan1, 2
  • Liu, Ding1, 2
  • Song, Zhuang3
  • Schuff, Norbert4, 5
  • Liang, Zhi-Pei1, 2
  • 1 Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • 2 Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA.
  • 3 Center for Vital Longevity, University of Texas at Dallas, Dallas, Texas, USA.
  • 4 Department of Radiology and Biomedical Imaging, University of California, San Francisco, California, USA.
  • 5 Department of Veterans Affairs Medical Center, Center for Imaging of Neurodegenerative Diseases, San Francisco, California, USA.
Type
Published Article
Journal
Magnetic Resonance in Medicine
Publisher
Wiley (John Wiley & Sons)
Publication Date
January 2016
Volume
75
Issue
1
Pages
433–440
Identifiers
DOI: 10.1002/mrm.25643
PMID: 25733066
Source
Medline
Keywords
License
Unknown

Abstract

The optimization problem associated with denoising noncentral χ distributed diffusion-weighted images subject to joint rank and edge constraints can be solved efficiently using a majorize-minimize-based algorithm.

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