SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space

Authors

  • Michael Lustig,

    Corresponding author
    1. Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, California, USA
    2. Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA
    • 506 Cory Hall, University of California, Berkeley, Berkeley, CA 94720
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  • John M. Pauly

    1. Magnetic Resonance Systems Research Laboratory, Department of Electrical Engineering, Stanford University, Stanford, California, USA
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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 ℓ1-wavelet regularization are also demonstrated. Magn Reson Med, 2010. © 2010 Wiley-Liss, Inc.

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