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Magnetic Resonance in Medicine
Article . 2002 . Peer-reviewed
License: Wiley Online Library User Agreement
Data sources: Crossref
MPG.PuRe
Article . 2002
Data sources: MPG.PuRe
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Generalized autocalibrating partially parallel acquisitions (GRAPPA)

Authors: Griswold, M.; Jakob, P.; Heidemann, R.; Nittka, M.; Jellus, V.; Wang, J.; Kiefer, B.; +1 Authors

Generalized autocalibrating partially parallel acquisitions (GRAPPA)

Abstract

AbstractIn this study, a novel partially parallel acquisition (PPA) method is presented which can be used to accelerate image acquisition using an RF coil array for spatial encoding. This technique, GeneRalized Autocalibrating Partially Parallel Acquisitions (GRAPPA) is an extension of both the PILS and VD‐AUTO‐SMASH reconstruction techniques. As in those previous methods, a detailed, highly accurate RF field map is not needed prior to reconstruction in GRAPPA. This information is obtained from several k‐space lines which are acquired in addition to the normal image acquisition. As in PILS, the GRAPPA reconstruction algorithm provides unaliased images from each component coil prior to image combination. This results in even higher SNR and better image quality since the steps of image reconstruction and image combination are performed in separate steps. After introducing the GRAPPA technique, primary focus is given to issues related to the practical implementation of GRAPPA, including the reconstruction algorithm as well as analysis of SNR in the resulting images. Finally, in vivo GRAPPA images are shown which demonstrate the utility of the technique. Magn Reson Med 47:1202–1210, 2002. © 2002 Wiley‐Liss, Inc.

Related Organizations
Keywords

Calibration, Image Processing, Computer-Assisted, Humans, Computer Simulation, Models, Theoretical, Magnetic Resonance Imaging, Algorithms

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
5K
Top 0.01%
Top 0.01%
Top 0.1%
bronze