TY - GEN
T1 - Smoothly clipped absolute deviation (SCAD) regularization for compressed sensing MRI using an augmented Lagrangian scheme
AU - Mehranian, A.
AU - Saligheh Rad, H.
AU - Ay, M. R.
AU - Rahmim, A.
AU - Zaidi, H.
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84881591263&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84881591263&partnerID=8YFLogxK
U2 - 10.1109/NSSMIC.2012.6551838
DO - 10.1109/NSSMIC.2012.6551838
M3 - Conference contribution
AN - SCOPUS:84881591263
SN - 9781467320306
T3 - IEEE Nuclear Science Symposium Conference Record
SP - 3646
EP - 3653
BT - 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
T2 - 2012 IEEE Nuclear Science Symposium and Medical Imaging Conference Record, NSS/MIC 2012
Y2 - 29 October 2012 through 3 November 2012
ER -