ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA

Martin Uecker; Peng Lai; Mark J. Murphy; Patrick Virtue; Michael Elad; John M. Pauly; Shreyas S. Vasanawala; Michael Lustig

doi:10.1002/mrm.24751

journal article Open Access May 06, 2013

ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA

Martin Uecker Peng Lai Mark J. Murphy Patrick Virtue Michael Elad

John M. Pauly

Shreyas S. Vasanawala Michael Lustig

Magnetic Resonance in Medicine Vol. 71 No. 3 pp. 990-1001 · Wiley

View at Publisher Save 10.1002/mrm.24751

Abstract

PurposeParallel imaging allows the reconstruction of images from undersampled multicoil data. The two main approaches are: SENSE, which explicitly uses coil sensitivities, and GRAPPA, which makes use of learned correlations in k‐space. The purpose of this work is to clarify their relationship and to develop and evaluate an improved algorithm.Theory and MethodsA theoretical analysis shows: (1) The correlations in k‐space are encoded in the null space of a calibration matrix. (2) Both approaches restrict the solution to a subspace spanned by the sensitivities. (3) The sensitivities appear as the main eigenvector of a reconstruction operator computed from the null space. The basic assumptions and the quality of the sensitivity maps are evaluated in experimental examples. The appearance of additional eigenvectors motivates an extended SENSE reconstruction with multiple maps, which is compared to existing methods.ResultsThe existence of a null space and the high quality of the extracted sensitivities are confirmed. The extended reconstruction combines all advantages of SENSE with robustness to certain errors similar to GRAPPA.ConclusionIn this article the gap between both approaches is finally bridged. A new autocalibration technique combines the benefits of both. Magn Reson Med 71:990–1001, 2014. © 2013 Wiley Periodicals, Inc.

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References

38

[1]

10.1002/mrm.1910300123

[2]

10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s

[3]

10.1002/mrm.10171

[4]

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

Michael Lustig, John M. Pauly

Magnetic Resonance in Medicine 10.1002/mrm.22428

[5]

10.1002/mrm.1241

[6]

10.1002/mrm.21012

[7]

10.1002/mrm.21236

[8]

10.1002/mrm.21391

[9]

10.1002/mrm.10087

[10]

10.1002/mrm.21245

[11]

10.1002/mrm.21691

[12]

Lai P (2010)

[13]

Lustig M (2011)

[14]

10.1002/mrm.20249

[15]

10.1002/mrm.20285

[16]

10.1002/jmri.20620

[17]

10.1002/mrm.24344

[18]

Uecker M (2009)

[19]

10.1002/mrm.22899

[20]

Griswold M (2002)

[21]

10.1109/tmi.2012.2188039

[22]

10.1109/jproc.2004.840301

[23]

Vasanawala SS (2011)

[24]

10.1002/mrm.24267

[25]

10.1111/j.1365-246x.1973.tb03424.x

[26]

10.1109/tap.1986.1143830

[27]

10.1109/29.32276

[28]

10.1109/83.743855

[29]

10.1109/tsp.2011.2166550

[30]

She H (2010)

[31]

Sharif B (2011)

[32]

Lustig M (2010)

[33]

Beatty PJ (2006)

[34]

Ding Y (2012)

[35]

10.1002/(sici)1522-2594(200005)43:5<682::aid-mrm10>3.0.co;2-g

[36]

Morrison RL (2007)

[37]

Larkman DJ (2005)

[38]

Uecker M (2012)

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References

Details

Published: May 06, 2013
Vol/Issue: 71(3)
Pages: 990-1001
License: View

Authors

M

Martin Uecker

Department of Electrical Engineering and Computer Sciences University of California Berkeley California USA

P

Peng Lai

Global Applied Science Laboratory GE Healthcare Menlo Park California USA

M

Mark J. Murphy

Department of Electrical Engineering and Computer Sciences University of California Berkeley California USA

P

Patrick Virtue

Department of Electrical Engineering and Computer Sciences University of California Berkeley California USA

M

Michael Elad

The Computer‐Science Department The Technion—Israel Institute of Technology Haifa Israel

J

John M. Pauly

Magnetic Resonance Systems Research Laboratory Department of Electrical Engineering Stanford University Stanford California USA

S

Shreyas S. Vasanawala

Department of Radiology Stanford University Stanford California USA

M

Michael Lustig

Department of Electrical Engineering and Computer Sciences University of California Berkeley California USA

Funding

NIH Award: P41RR09784

UC Discovery Award: 193037

GE Healthcare

Cite This Article

Martin Uecker, Peng Lai, Mark J. Murphy, et al. (2013). ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA. Magnetic Resonance in Medicine, 71(3), 990-1001. https://doi.org/10.1002/mrm.24751

ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: Where SENSE meets GRAPPA

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