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Title

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

Authors
Rad, Hamidreza Saligheh
Rahmim, Arman
Published in Magnetic Resonance Imaging. 2013, vol. 31, no. 8, p. 1399-411
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 AlgorithmsArtifactsBrain/anatomy & histologyData Compression/methodsHumansImage Enhancement/methodsImage Interpretation, Computer-Assisted/methodsMagnetic Resonance Imaging/methodsNumerical Analysis, Computer-AssistedReproducibility of ResultsSample SizeSensitivity and Specificity
Identifiers
PMID: 23891179
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Article (Published version) (3.6 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Research group Imagerie Médicale (TEP et TEMP) (542)
Project FNS: 31003A-149957
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. 1399-411. https://archive-ouverte.unige.ch/unige:40169

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Deposited on : 2014-09-12

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