• Mark A Griswold, Felix Breuer, Martin Blaimer, Stephan Kannengiesser, Robin M Heidemann, Matthias Mueller, Mathias Nittka, Vladimir Jellus, Berthold Kiefer, and Peter M Jakob.
• University of Würzburg, Department of Physics, EP5, Am Hubland, 97074 Würzburg, Germany. griswold@uhrad.com
• NMR Biomed. 2006 May 1; 19 (3): 316-24.

AbstractParallel imaging has proven to be a robust solution to the problem of acquisition speed in MRI. These methods are based on extracting spatial information from an array of multiple surface coils in order to speed up image acquisition. One of the most essential elements of any parallel imaging method is the information describing the coil sensitivity distribution throughout the sample. This paper covers some of the advanced methods to obtain coil sensitivity-related information, focusing particularly on the class of methods referred to as autocalibrating. These methods all acquire the data for coil sensitivity estimation directly before, during or directly after the reduced data acquisition. After a review of standard methods for coil sensitivity estimation, some of the basic and advanced autocalibrating methods are reviewed, and some example applications shown.Copyright (c) 2006 John Wiley & Sons, Ltd.

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