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Regularization method for phase‐constrained parallel MRI
Author(s) -
Blaimer Martin,
Jakob Peter M.,
Breuer Felix A.
Publication year - 2014
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.24896
Subject(s) - regularization (linguistics) , computer science , algorithm , artificial intelligence , computer vision
Purpose To implement a regularization method for the phase‐constrained generalized partially parallel acquisitions (GRAPPA) algorithm to reduce image artifacts caused by data inconsistencies. Methods Phase‐constrained GRAPPA reconstructions are implemented through the use of virtual coils. To that end, synthetic virtual coils are generated by using complex conjugate symmetric signals from the actual coils. Regularization is achieved by applying coefficient‐based penalty factors during the GRAPPA calibration procedure. Different penalizing factors are applied for the actual and virtual coils. The method is tested in vivo using T2‐weighted turbo spin echo (TSE) images. Results T2 signal decay perturbs conjugate k‐space symmetry and produces artifacts in phase‐constrained parallel MRI reconstructions of T2‐weighted TSE images. Using the proposed regularization method, artifacts are suppressed at the cost of noise amplification. However, there is still a significant SNR gain compared with conventional GRAPPA. Conclusion The proposed regularization method is an efficient approach for artifact suppression and maintains the SNR benefit of phase‐constrained parallel MRI over conventional parallel MRI. Magn Reson Med 72:166–171, 2014. © 2013 Wiley Periodicals, Inc .