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Scientific article
English

Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme

Published inMagnetic resonance imaging, vol. 31, no. 8, p. 1399-1411
Publication date2013
Abstract

Compressed sensing (CS) provides a promising framework for MR image reconstruction from highly undersampled data, thus reducing data acquisition time. In this context, sparsity-promoting regularization techniques exploit the prior knowledge that MR images are sparse or compressible in a given transform domain. In this work, a new regularization technique was introduced by iterative linearization of the non-convex smoothly clipped absolute deviation (SCAD) norm with the aim of reducing the sampling rate even lower than it is required by the conventional l1 norm while approaching an l0 norm.

Keywords
  • Algorithms
  • Artifacts
  • Brain/anatomy & histology
  • Data Compression/methods
  • Humans
  • Image Enhancement/methods
  • Image Interpretation, Computer-Assisted/methods
  • Magnetic Resonance Imaging/methods
  • Numerical Analysis, Computer-Assisted
  • Reproducibility of Results
  • Sample Size
  • Sensitivity and Specificity
Citation (ISO format)
MEHRANIAN, Abolfazl et al. Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme. In: Magnetic resonance imaging, 2013, vol. 31, n° 8, p. 1399–1411. doi: 10.1016/j.mri.2013.05.010
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ISSN of the journal0730-725X
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Creation08/06/2014 1:07:00 PM
First validation08/06/2014 1:07:00 PM
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