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Parallel MRI with extended and averaged GRAPPA kernels (PEAK‐GRAPPA): Optimized spatiotemporal dynamic imaging
Author(s) -
Jung Bernd,
Ullmann Peter,
Honal Matthias,
Bauer Simon,
Hennig Jürgen,
Markl Michael
Publication year - 2008
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.21561
Subject(s) - computer science , dynamic contrast enhanced mri , dynamic imaging , dynamic contrast , magnetic resonance imaging , artificial intelligence , radiology , medicine , image processing , image (mathematics) , digital image processing
Purpose To evaluate an optimized k‐t‐space related reconstruction method for dynamic magnetic resonance imaging (MRI), a method called PEAK‐GRAPPA (Parallel MRI with Extended and Averaged GRAPPA Kernels) is presented which is based on an extended spatiotemporal GRAPPA kernel in combination with temporal averaging of coil weights. Materials and Methods The PEAK‐GRAPPA kernel consists of a uniform geometry with several spatial and temporal source points from acquired k‐space lines and several target points from missing k‐space lines. In order to improve the quality of coil weight estimation sets of coil weights are averaged over the temporal dimension. Results The kernel geometry leads to strongly decreased reconstruction times compared to the recently introduced k‐t‐GRAPPA using different kernel geometries with only one target point per kernel to fit. Improved results were obtained in terms of the root mean square error and the signal‐to‐noise ratio as demonstrated by in vivo cardiac imaging. Conclusion Using a uniform kernel geometry for weight estimation with the properties of uncorrelated noise of different acquired timeframes, optimized results were achieved in terms of error level, signal‐to‐noise ratio, and reconstruction time. J. Magn. Reson. Imaging 2008;28:1226–1232. © 2008 Wiley‐Liss, Inc.