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SPIRiT: Iterative self‐consistent parallel imaging reconstruction from arbitrary k ‐space
Author(s) -
Lustig Michael,
Pauly John M.
Publication year - 2010
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.22428
Subject(s) - iterative reconstruction , imaging phantom , regularization (linguistics) , compressed sensing , computer science , algorithm , inverse problem , iterative method , k space , conjugate gradient method , prior probability , mathematics , artificial intelligence , mathematical optimization , computer vision , bayesian probability , fourier transform , mathematical analysis , physics , optics
Abstract A new approach to autocalibrating, coil‐by‐coil parallel imaging reconstruction, is presented. It is a generalized reconstruction framework based on self‐consistency. The reconstruction problem is formulated as an optimization that yields the most consistent solution with the calibration and acquisition data. The approach is general and can accurately reconstruct images from arbitrary k ‐space sampling patterns. The formulation can flexibly incorporate additional image priors such as off‐resonance correction and regularization terms that appear in compressed sensing. Several iterative strategies to solve the posed reconstruction problem in both image and k ‐space domain are presented. These are based on a projection over convex sets and conjugate gradient algorithms. Phantom and in vivo studies demonstrate efficient reconstructions from undersampled Cartesian and spiral trajectories. Reconstructions that include off‐resonance correction and nonlinear ℓ 1 ‐wavelet regularization are also demonstrated. Magn Reson Med, 2010. © 2010 Wiley‐Liss, Inc.