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Optimized parallel imaging for dynamic PC‐MRI with multidirectional velocity encoding
Author(s) -
Peng HsuHsia,
Bauer Simon,
Huang TengYi,
Chung HsiaoWen,
Hennig Jürgen,
Jung Bernd,
Markl Michael
Publication year - 2010
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.22432
Subject(s) - encoding (memory) , computer science , dynamic contrast enhanced mri , nuclear magnetic resonance , magnetic resonance imaging , physics , artificial intelligence , medicine , radiology
Phase contrast MRI with multidirectional velocity encoding requires multiple acquisitions of the same k ‐space lines to encode the underlying velocities, which can considerably lengthen the total scan time. To reduce scan time, parallel imaging is often applied. In dynamic phase contrast MRI using standard generalized autocalibrating partially parallel acquisitions (GRAPPA), several central k ‐spaces for autocalibration of the reconstruction (autocalibrating signal lines (ACS)) are typically acquired, separately for each velocity direction and each cardiac timeframe, for calculating the reconstruction weights. To further accelerate data acquisition, we developed two methods, which calculated weights with a substantially reduced number of ACSl lines. The effects on image quality and flow quantification were compared to fully sampled data, standard GRAPPA, and time‐interleaved sampling scheme in combination with generalized autocalibrating partially parallel acquisitions (TGRAPPA). The results show that the two proposed methods can clearly improve scan efficiency while maintaining image quality and accuracy of measured flow or myocardial tissue velocities. Compared to TGRAPPA, the proposed methods were more accurate in evaluating flow velocity. In conclusion, the proposed reconstruction strategies are promising for dynamic multidirectionally encoded acquisitions and can easily be implemented using the standard GRAPPA reconstruction algorithm. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.

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