Reference-Driven Compressed Sensing MR Image Reconstruction with Partially Known Support and Group Sparsity Constraints

DOI

10.2991/icmt-13.2013.42How to use a DOI?

Keywords

Compressed Sensing·MR image reconstruction·The reference image·Support·Group sparsity

Abstract

Applying compressed sensing (CS) to magnetic resonance imaging (MRI) makes it possible to reconstruct a MR image from undersampled data. Traditional CS based MR image reconstruction schemes only use the signals’ sparsity in an appropriate transform domain to reduce sampling rate. This paper proposes a new MR image reconstruction method which utilizes structure features of the image besides sparsity. The proposed method exploits the distribution of the wavelet coefficients’ magnitudes and support information to formulate the objective function by minimizing which the image is reconstructed. The objective function consists of the data consistence term and two regularization terms: l1-l2 norm of the groups which contains the coefficients with the unknown support and total variation (TV) norm of the image. The simulation results from real MR images show that the proposed method outperforms the conventional CS based MR image reconstruction method.

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Dong Yize
AU  - Du Huiqian
AU  - Zhao Di
AU  - Han Yu
AU  - Mei Wenbo
PY  - 2013/11
DA  - 2013/11
TI  - Reference-Driven Compressed Sensing MR Image Reconstruction with Partially Known Support and Group Sparsity Constraints
BT  - Proceedings of 3rd International Conference on Multimedia Technology(ICMT-13)
PB  - Atlantis Press
SP  - 344
EP  - 351
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmt-13.2013.42
DO  - 10.2991/icmt-13.2013.42
ID  - Yize2013/11
ER  -