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Title

Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme
Authors
Mehranian, Abolfazl
Rad, Hamidreza Saligheh
Rahmim, Arman
Ay, Mohammad Reza
Zaidi, Habib
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 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
Identifiers
PMID: 23891179
Full text
Article (Published version) (3.6 MB) - document accessible for UNIGE members only Limited access to UNIGE
Structures
Faculté de médecine / Section de médecine clinique / Département de radiologie et informatique médicale
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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