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Dynamic autocalibrated parallel imaging using temporal GRAPPA (TGRAPPA)
Author(s) -
Breuer Felix A.,
Kellman Peter,
Griswold Mark A.,
Jakob Peter M.
Publication year - 2005
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.20430
Subject(s) - computer science , electromagnetic coil , reference frame , data acquisition , sensitivity (control systems) , set (abstract data type) , frame (networking) , data set , temporal resolution , computer vision , calibration , sampling (signal processing) , signal (programming language) , artificial intelligence , algorithm , electronic engineering , mathematics , telecommunications , optics , statistics , physics , filter (signal processing) , electrical engineering , programming language , engineering , operating system
Current parallel imaging techniques for accelerated imaging require a fully encoded reference data set to estimate the spatial coil sensitivity information needed for reconstruction. In dynamic parallel imaging a time‐interleaved acquisition scheme can be used, which eliminates the need for separately acquiring additional reference data, since the signal from directly adjacent time frames can be merged to build a set of fully encoded full‐resolution reference data for coil calibration. In this work, we demonstrate that a time‐interleaved sampling scheme, in combination with autocalibrated GRAPPA (referred to as TGRAPPA), allows one to easily update the coil weights for the GRAPPA algorithm dynamically, thereby improving the acquisition efficiency. This method may update coil sensitivity estimates frame by frame, thereby tracking changes in relative coil sensitivities that may occur during the data acquisition. Magn Reson Med 53:981–985, 2005. Published 2005 Wiley‐Liss, Inc.