Abstract
In Compressed Sensing based Magnetic Resonance Imaging (CS-MRI) reconstruction, wavelet transform finds the widest application as a sparsifying transform. It has been reported that along with the standard wavelet sparsity, group sparsity terms, like, the tree sparsity, the joint sparsity and the forest sparsity exist in the wavelet decomposition of multi-channel, multi-slice, and multi-contrast MR images. To develop an efficient unified CS-MRI reconstruction model for these images, different wavelet based sparsity priors together plays a pivotal role. In this paper, we propose a simultaneous weighted forest sparsity and joint total variation based efficient CS-MRI reconstruction model for multi-channel, multi-slice, and multi-contrast MR images. The proposed technique shows significant improvements in terms of quality of reconstruction over existing methods for different datasets.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
GNRC Hospital, Sixmile, Guwahati, India; www.gnrchospitals.com.
References
Candes, E., Wakin, M., Boyd, S.: Enhancing sparsity by reweighted L1 minimization. J. Fourier Anal. Appl. 14(5), 877–905 (2008)
Candes, E.J., Romberg, J.K., Tao, T.: Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans. Inf. Theory 52(2), 489–509 (2006)
Chen, C., Huang, J.: Exploiting the wavelet structure in compressed sensing MRI. Magn. Reson. Imaging 32, 1377–1389 (2014)
Chen, C., Li, Y., Huang, J.: Forest sparsity for multi-channel compressive sensing. IEEE Trans. Sig. Process. 62(11), 2803–2813 (2014)
Datta, S., Deka, B.: Efficient adaptive weighted minimization for compressed sensing magnetic resonance image reconstruction. In: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP’16, pp. 95:1–95:8. ACM, New York, NY, USA (2016)
Datta, S., Deka, B.: Magnetic resonance image reconstruction using fast interpolated compressed sensing. J. Opt. (2017)
Datta, S., Deka, B., Mullah, H.U., Kumar, S.: An efficient interpolated compressed sensing method for highly correlated 2D multi-slice MRI. In: 2016 International Conference on Accessibility to Digital World (ICADW), pp. 187–192 (2016)
Deka, B., Datta, S.: Weighted wavelet tree sparsity regularization for compressed sensing magnetic resonance image reconstruction, pp. 449–457. Springer, Singapore (2017)
Huang, J., Chen, C., Axel, L.: Fast multi-contrast MRI reconstruction. Magn. Reson. Imaging 32(10), 1344–1352 (2014)
Huang, J., Zhang, S., Metaxas, D.N.: Efficient MR image reconstruction for compressed MR imaging. Med. Image Anal. 15(5), 670–679 (2011)
Lustig, M., Donoho, D., Pauly, J.M.: Sparse MRI: the application of compressed sensing for rapid MR imaging. Magn. Reson. Med. 58, 1182–1195 (2007)
Rohlfing, T., Zahr, N., Sullivan, E., Pfefferbaum, A.: The SRI24 multi-channel atlas of normal adult human brain structure. Hum. Brain Mapp. 31(5), 798–819 (2010)
Acknowledgement
Authors would like to thank Dr. S. K. Handique, MD (Radiodiagnosis), GNRC Hospitals, Guwahati, India for providing different 3D MRI data sets for simulation.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Datta, S., Deka, B. (2019). Multi-channel, Multi-slice, and Multi-contrast Compressed Sensing MRI Using Weighted Forest Sparsity and Joint TV Regularization Priors. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_65
Download citation
DOI: https://doi.org/10.1007/978-981-13-1592-3_65
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1591-6
Online ISBN: 978-981-13-1592-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)