title: A nonlinear regularization strategy for GRAPPA calibration.
abstract: A regularization strategy is described for generalized autocalibrating partially parallel acquisition (GRAPPA) that allows successful calibration using a small number of autocalibration signals (ACS). The approach requires certain nonlinear relationships between the GRAPPA coefficients to be satisfied, which increases the redundancy so that fewer ACS need to be acquired.
authors.affiliation: Department of Radiology, University of California San Diego, San Diego, CA 92103-8226, USA. mbydder@ucsd.edu
authors.forename: Mark
authors.initials: M
authors.lastname: Bydder
authors.forename: Youngkyoo
authors.initials: Y
authors.lastname: Jung
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D002138:Calibration; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging / Q000379:methods*