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

A. Mehranian, H. Saligheh Rad, M. R. Ay, A. Rahmim, H. Zaidi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Compressed sensing (CS) in magnetic resonance imaging (MRI) enables the reconstruction of MR images from highly undersampled k-spaces, and thus substantial reduction of data acquisition time. In this context, edge-preserving and sparsity-promoting regularizers are used to exploit the prior knowledge that MR images are sparse or compressible in a given transform domain and thus to regulate the solution space. In this study, we introduce a new regularization scheme by iterative linearization of the non-convex clipped absolute deviation (SCAD) function in an augmented Lagrangian framework. The performance of the proposed regularization, which turned out to be an iteratively weighted total variation (TV) regularization, was evaluated using 2D phantom simulations and 3D retrospective undersampling of clinical MRI data by different sampling trajectories. It was demonstrated that the proposed regularization technique substantially outperforms conventional TV regularization, especially at reduced sampling rates.

Original languageEnglish (US)
Title of host publication2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Pages3646-3653
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012 - Anaheim, CA, United States
Duration: Oct 29 2012Nov 3 2012

Publication series

NameIEEE Nuclear Science Symposium Conference Record
ISSN (Print)1095-7863

Other

Other2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
CountryUnited States
CityAnaheim, CA
Period10/29/1211/3/12

ASJC Scopus subject areas

  • Radiation
  • Nuclear and High Energy Physics
  • Radiology Nuclear Medicine and imaging

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