Paper Titles
Research on Continuous Phase FM Signal Demodulation Based on STFT
p.3930
An Adaptive Filtering Method for Chirp Signal Based on FRFT
p.3934
Research on FM Telemetry Software Demodulation Algorithms
p.3938
Performance Analysis on Three Methods for Chirp Signal Parameters Estimation Based on FRFT
p.3942
Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction
p.3946
Pulse Coding Soft FM Telemetry Signal Demodulation
p.3952
Design of High-Speed Data Collector of Blast Wave Signal Based on the Post-Trigger Principle
p.3956
On Matlab’s Simulations in Underground Mobile Multipath Channel
p.3960
Clutter Modeling and Characteristic Analysis for Airborne MIMO Radar
p.3963

Group Sparsity Regularization Based Compressed Sensing MR Image Reconstruction

Article Preview

Abstract:

Exploiting the sparsity of MR signals, Compressed Sensing MR imaging (CS-MRI) is one of the most promising approaches to reconstruct a MR image with good quality from highly under-sampled k-space data. The group sparse method, which exploits additional sparse representation of the spatial group structure, can promote the overall sparsity degree, thereby leading to better reconstruction performance. In this work, an efficient superpixel/group assignment method, simple linear iterative clustering (SLIC), is incorporated to CS-MRI studies. A variable splitting strategy and classic alternating direct method is employed to solve the group sparse problem. The results indicate that the proposed method is capable of achieving significant improvements in reconstruction accuracy when compared with the state-of-the-art reconstruction methods.

You might also be interested in these eBooks
Materials Science, Computer and Information Technology View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 989-994)

Pages:

3946-3951

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.989-994.3946

Citation:

Cite this paper

Online since:

July 2014

Authors:

Xin Jin*, Ming Feng Jiang, Jie Feng

Keywords:

Group Sparse, Mr Image Reconstruction, Superpixel

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] C. Westbrook, C. Roth K. and J. Talbot. MRI in Practice. 3rd edn, New York: Wiley-Blackwell, (2005).

Google Scholar

[2] E. J. Candès, J. Romberg, T. Tao, Robust uncertainty principles: Exact signal reconstruction from highly incomplete frequency information, IEEE Trans. on Info. Theory. 52 (2006) 489-509.

DOI: 10.1109/tit.2005.862083

Google Scholar

[3] D. L. Donoho, Compressed sensing, IEEE Trans. on Info. Theory. 52 (2006) 1289-1306.

Google Scholar

[4] M. Lustig, D. L. Donoho, and J. M. Pauly, Sparse MRI: the application of compressed sensing for rapid MR Imaging, Magn. Reson. Med. 58 (2007) 1182-1195.

DOI: 10.1002/mrm.21391

Google Scholar

[5] J. Huang, S. Zhang, D. Metaxas, Efficient MR image reconstruction for compressed MR imaging, Med. Image Anal. 15 (2011) 670-679.

DOI: 10.1016/j.media.2011.06.001

Google Scholar

[6] H. Du, F. Lam, Compressed sensing MR image reconstruction using a motion-compensated reference, Magn. Reson. Imaging. 30 (2012) 954-963.

DOI: 10.1016/j.mri.2012.03.005

Google Scholar

[7] M. Stojnic, F. Parvaresh, B. Hassibi, On the reconstruction of block sparse signals with an optimal number of measurements, IEEE Trans. Signal Process. 57 (2009) 3075–3085.

DOI: 10.1109/tsp.2009.2020754

Google Scholar

[8] W. Deng, W. Ying, and Y. Zhang, Group sparse optimization by alternating direction method, Technical Report TR11-06, Department of Computational and Applied Mathematics, Rice University, (2011).

Google Scholar

[9] M. Usman, C. Prieto, T. Schaeffter, and P. G. Batchelor, k-t Group Sparse: A Method for Accelerating Dynamic MRI, Magn. Reson. Med. 66 (2011) 1163–1176.

DOI: 10.1002/mrm.22883

Google Scholar

[10] C. Prieto, M. Usman, J. M. Wild, S. Kozerke, P. G. Batchelor, and T. Schaeffter, Group Sparse Reconstruction Using Intensity-Based Clustering, Magn. Reson. Med., 69 (2013) 1169-1179.

DOI: 10.1002/mrm.24333

Google Scholar

[11] R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua, and S. Süsstrunk, SLIC Superpixels Compared to State-of-the-art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence. 34 (2012) 2274-2282.

DOI: 10.1109/tpami.2012.120

Google Scholar

[12] Y. Xu, W. Yin, Z. Wen, and Y. Zhang, An Alternating Direction Algorithm for Matrix Completion with Nonnegative Factors, Front. Math. China. 7 (2012) 365-384.

DOI: 10.1007/s11464-012-0194-5

Google Scholar