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Using GRAPPA to improve autocalibrated coil sensitivity estimation for the SENSE family of parallel imaging reconstruction algorithms
Author(s) -
Hoge W. Scott,
Brooks Dana H.
Publication year - 2008
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.21634
Subject(s) - electromagnetic coil , computer science , leverage (statistics) , sensitivity (control systems) , regularization (linguistics) , sense (electronics) , algorithm , image quality , artificial intelligence , iterative reconstruction , nyquist rate , calibration , computer vision , image (mathematics) , mathematics , statistics , sampling (signal processing) , electronic engineering , physics , electrical engineering , quantum mechanics , engineering , filter (signal processing)
Two strategies are widely used in parallel MRI to reconstruct subsampled multicoil image data. SENSE and related methods employ explicit receiver coil spatial response estimates to reconstruct an image. In contrast, coil‐by‐coil methods such as GRAPPA leverage correlations among the acquired multicoil data to reconstruct missing k ‐space lines. In self‐referenced scenarios, both methods employ Nyquist‐rate low‐frequency k ‐space data to identify the reconstruction parameters. Because GRAPPA does not require explicit coil sensitivities estimates, it needs considerably fewer autocalibration signals than SENSE. However, SENSE methods allow greater opportunity to control reconstruction quality though regularization and thus may outperform GRAPPA in some imaging scenarios. Here, we employ GRAPPA to improve self‐referenced coil sensitivity estimation in SENSE and related methods using very few auto‐calibration signals. This enables one to leverage each methods' inherent strength and produce high quality self‐referenced SENSE reconstructions. Magn Reson Med 60:462–467, 2008. © 2008 Wiley‐Liss, Inc.