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Exploiting the wavelet structure in compressed sensing MRI.
Magn Reson Imaging. 2014 Dec; 32(10):1377-89.MR

Abstract

Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms.

Authors+Show Affiliations

Department of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Boulevard, Arlington, TX, 76019.Department of Computer Science and Engineering, University of Texas at Arlington, 500 UTA Boulevard, Arlington, TX, 76019. Electronic address: jzhuang@uta.edu.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

25153483

Citation

Chen, Chen, and Junzhou Huang. "Exploiting the Wavelet Structure in Compressed Sensing MRI." Magnetic Resonance Imaging, vol. 32, no. 10, 2014, pp. 1377-89.
Chen C, Huang J. Exploiting the wavelet structure in compressed sensing MRI. Magn Reson Imaging. 2014;32(10):1377-89.
Chen, C., & Huang, J. (2014). Exploiting the wavelet structure in compressed sensing MRI. Magnetic Resonance Imaging, 32(10), 1377-89. https://doi.org/10.1016/j.mri.2014.07.016
Chen C, Huang J. Exploiting the Wavelet Structure in Compressed Sensing MRI. Magn Reson Imaging. 2014;32(10):1377-89. PubMed PMID: 25153483.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - Exploiting the wavelet structure in compressed sensing MRI. AU - Chen,Chen, AU - Huang,Junzhou, Y1 - 2014/08/19/ PY - 2013/12/28/received PY - 2014/03/16/revised PY - 2014/07/24/accepted PY - 2014/8/26/entrez PY - 2014/8/26/pubmed PY - 2015/8/1/medline KW - Compressed sensing MRI KW - Sparse MRI KW - Structured sparsity KW - Tree sparsity KW - Wavelet tree structure SP - 1377 EP - 89 JF - Magnetic resonance imaging JO - Magn Reson Imaging VL - 32 IS - 10 N2 - Sparsity has been widely utilized in magnetic resonance imaging (MRI) to reduce k-space sampling. According to structured sparsity theories, fewer measurements are required for tree sparse data than the data only with standard sparsity. Intuitively, more accurate image reconstruction can be achieved with the same number of measurements by exploiting the wavelet tree structure in MRI. A novel algorithm is proposed in this article to reconstruct MR images from undersampled k-space data. In contrast to conventional compressed sensing MRI (CS-MRI) that only relies on the sparsity of MR images in wavelet or gradient domain, we exploit the wavelet tree structure to improve CS-MRI. This tree-based CS-MRI problem is decomposed into three simpler subproblems then each of the subproblems can be efficiently solved by an iterative scheme. Simulations and in vivo experiments demonstrate the significant improvement of the proposed method compared to conventional CS-MRI algorithms, and the feasibleness on MR data compared to existing tree-based imaging algorithms. SN - 1873-5894 UR - https://www.unboundmedicine.com/medline/citation/25153483/Exploiting_the_wavelet_structure_in_compressed_sensing_MRI_ L2 - https://linkinghub.elsevier.com/retrieve/pii/S0730-725X(14)00243-4 DB - PRIME DP - Unbound Medicine ER -