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k ‐ t ISD: Dynamic cardiac MR imaging using compressed sensing with iterative support detection
Author(s) -
Liang Dong,
DiBella Edward V. R.,
Chen RongRong,
Ying Leslie
Publication year - 2012
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.23197
Subject(s) - compressed sensing , thresholding , computer science , iterative reconstruction , iterative method , dynamic contrast enhanced mri , image (mathematics) , artificial intelligence , computer vision , image quality , cardiac imaging , algorithm , minification , magnetic resonance imaging , radiology , medicine , programming language
Compressed sensing (CS) has been used in dynamic cardiac MRI to reduce the data acquisition time. The sparseness of the dynamic image series in the spatial‐ and temporal‐frequency ( x‐f ) domain has been exploited in existing works. In this article, we propose a new k ‐ t iterative support detection ( k ‐ t ISD) method to improve the CS reconstruction for dynamic cardiac MRI by incorporating additional information on the support of the dynamic image in x‐f space based on the theory of CS with partially known support. The proposed method uses an iterative procedure for alternating between image reconstruction and support detection in x‐f space. At each iteration, a truncated ℓ 1 minimization is applied to obtain the reconstructed image in x‐f space using the support information from the previous iteration. Subsequently, by thresholding the reconstruction, we update the support information to be used in the next iteration. Experimental results demonstrate that the proposed k‐t ISD method improves the reconstruction quality of dynamic cardiac MRI over the basic CS method in which support information is not exploited. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.