Scientific article
English

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

Published in

Magnetic resonance imaging, vol. 31, no. 8, p. 1399-1411

Publication date

2013

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

Main files (1)

Article (Published version)

Identifiers

Journal ISSN

0730-725X
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Technical informations

Creation

06/08/2014 13:07:00

First validation

06/08/2014 13:07:00

Update

15/11/2024 15:40:58

Status update

15/11/2024 15:40:58

Last indexation

15/11/2024 15:43:39
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