Full Paper

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

  1. Michael Lustig1,2,*,
  2. John M. Pauly2

Article first published online: 1 JUN 2010

DOI: 10.1002/mrm.22428

Issue

Magnetic Resonance in Medicine

Magnetic Resonance in Medicine

Volume 64, Issue 2, pages 457–471, August 2010

Additional Information

How to Cite

Lustig, M. and Pauly, J. M. (2010), SPIRiT: Iterative self-consistent parallel imaging reconstruction from arbitrary k-space. Magn Reson Med, 64: 457–471. doi: 10.1002/mrm.22428

Author Information

  1. 1

    Department of Electrical Engineering and Computer Science, University of California at Berkeley, Berkeley, California, USA

  2. 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

Publication History

  1. Issue published online: 20 JUL 2010
  2. Article first published online: 1 JUN 2010
  3. Manuscript Accepted: 11 FEB 2010
  4. Manuscript Received: 13 JAN 2010
  5. Manuscript Revised: 13 JAN 2010

Funded by

  • NIH. Grant Numbers: P41RR09784, R01EB007588, R21EB007715

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Keywords:

  • image reconstruction;
  • autocalibration;
  • parallel imaging;
  • compressed sensing;
  • SENSE;
  • GRAPPA;
  • iterative reconstruction

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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