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2D‐GRAPPA‐operator for faster 3D parallel MRI
Author(s) -
Blaimer Martin,
Breuer Felix A.,
Mueller Matthias,
Seiberlich Nicole,
Ebel Dmitry,
Heidemann Robin M.,
Griswold Mark A.,
Jakob Peter M.
Publication year - 2006
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.21071
Subject(s) - computer science , dimension (graph theory) , operator (biology) , algorithm , encoding (memory) , artificial intelligence , sensitivity (control systems) , process (computing) , domain (mathematical analysis) , fourier transform , computer vision , pattern recognition (psychology) , mathematics , mathematical analysis , biochemistry , chemistry , repressor , electronic engineering , transcription factor , pure mathematics , engineering , gene , operating system
When using parallel MRI (pMRI) methods in combination with three‐dimensional (3D) imaging, it is beneficial to subsample the k ‐space along both phase‐encoding directions because one can then take advantage of coil sensitivity variations along two spatial dimensions. This results in an improved reconstruction quality and therefore allows greater scan time reductions as compared to subsampling along one dimension. In this work we present a new approach based on the generalized autocalibrating partially parallel acquisitions (GRAPPA) technique that allows Fourier‐domain reconstructions of data sets that are subsampled along two dimensions. The method works by splitting the 2D reconstruction process into two separate 1D reconstructions. This approach is compared with an extension of the conventional GRAPPA method that directly regenerates missing data points of a 2D subsampled k ‐space by performing a linear combination of acquired data points. In this paper we describe the theoretical background and present computer simulations and in vivo experiments. Magn Reson Med, 2006. © 2006 Wiley‐Liss, Inc.