Improving GRAPPA reconstruction by frequency discrimination in the ACS lines

  • Santiago Aja-Fernández
  • Daniel García Martín
  • Antonio Tristán-Vega
  • Gonzalo Vegas-Sánchez-Ferrero
Original Article

Abstract

Purpose

GRAPPA is a well-known parallel imaging method that recovers the MR magnitude image from aliasing by using a weighted interpolation of the data in k-space. To estimate the optimal reconstruction weights, GRAPPA uses a band along the center of the k-space where the signal is sampled at the Nyquist rate, the so-called autocalibrated (ACS) lines. However, while the subsampled lines usually belong to the medium- to high-frequency areas of the spectrum, the ACS lines include the low-frequency areas around the DC component. The use for estimation and reconstruction of areas of the k-space with very different features may negatively affect the final reconstruction quality. We propose a simple, yet powerful method to eliminate reconstruction artifacts, based on the discrimination of the low-frequency spectrum.

Methods

The proposal to improve the estimation of the weights lays on a proper selection of the coefficients within the ACS lines, which advises discarding those points around the DC component. A simple approach is the elimination of a square window in the center of the k-space, although more developed approaches can be used.

Results

The method is tested using real multiple-coil MRI acquisitions. We empirically show this approach achieves great enhancement rates, while keeping the same complexity of the original GRAPPA and reducing the g-factor. The reconstruction is even more accurate when combined with other reconstruction methods. Improvement rates of 35 % are achieved for 32 ACS and acceleration rate of 3.

Conclusions

The method proposed highly improves the accuracy of the GRAPPA coefficients and therefore the final image reconstruction. The method is fully compatible with the original GRAPPA formulation and with other optimization methods proposed in literature, and it can be easily implemented into the commercial scanning software.

Keywords

MRI GRAPPA Parallel imaging Reconstruction Estimation 

References

  1. 1.
    Blaimer M, Breuer F, Mueller M, Heidemann R, Griswold M, Jakob P (2004) SMASH, SENSE, PILS, GRAPPA: how to choose the optimal method. Top Magn Reson Imaging 15(4):223–236CrossRefPubMedGoogle Scholar
  2. 2.
    Breuer FA, Kannengiesser SA, Blaimer M, Seiberlich N, Jakob PM, Griswold MA (2009) General formulation for quantitative g-factor calculation in GRAPPA reconstructions. Magn Reson Med 62(3):739–746CrossRefPubMedGoogle Scholar
  3. 3.
    Chang Y, Liang D, Ying L (2012) Nonlinear GRAPPA: a kernel approach to parallel MRI reconstruction. Magn Reson Med 68(3):730–740CrossRefPubMedGoogle Scholar
  4. 4.
    Chen Z, Zhang J, Yang R, Kellman P, Johnston LA, Egan GF (2010) IIR GRAPPA for parallel MR image reconstruction. Magn Reson Med 63(2):502–509CrossRefPubMedGoogle Scholar
  5. 5.
    Constantinides C, Atalar E, McVeigh E (1997) Signal-to-noise measurements in magnitude images from NMR phased arrays. Magn Reson Med 38:852–857PubMedCentralCrossRefPubMedGoogle Scholar
  6. 6.
    Ding Y, Xue H, Ahmad R, Chang Tc, Ting ST, Simonetti OP (2014) Paradoxical effect of the signal-to-noise ratio of GRAPPA calibration lines: a quantitative study. Magn Reson Med. doi:10.1002/mrm.25385
  7. 7.
    Eskicioglu A, Fisher P (1995) Image quality measures and their performance. IEEE Trans Commun 43(12):2959–2965CrossRefGoogle Scholar
  8. 8.
    Griswold MA, Jakob PM, Heidemann RM, Nittka M, Jellus V, Wang J, Kiefer B, Haase A (2002) Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn Reson Med 47(6):1202–1210CrossRefPubMedGoogle Scholar
  9. 9.
    Hoge WS, Brooks DH, Madore B, Kyriakos WE (2005) A tour of accelerated parallel MR imaging from a linear systems perspective. Concepts Magn Reson Part A 27A(1):17–37CrossRefGoogle Scholar
  10. 10.
    Huang F, Li Y, Vijayakumar S, Hertel S, Duensing GR (2008) High-pass GRAPPA: an image support reduction technique for improved partially parallel imaging. Magn Reson Med 59(3):642–649CrossRefPubMedGoogle Scholar
  11. 11.
    Huo D, Wilson D (2008) Robust GRAPPA reconstruction and its evaluation with the perceptual difference model. J Magn Reson 27(6):1412–1420CrossRefGoogle Scholar
  12. 12.
    Ji JX, Son JB, Rane SD (2007) PULSAR: a matlab toolbox for parallel magnetic resonance imaging using array coils and multiple channel receivers. Concepts in Magn Reson Part B Magn Reson Eng 31B(1):24–36CrossRefGoogle Scholar
  13. 13.
    Lim JS (1990) Two dimensional signal and image processing. Prentice Hall, Englewood CliffsGoogle Scholar
  14. 14.
    Park S, Park J (2012) Adaptive self-calibrating iterative GRAPPA reconstruction. Magn Reson Med 67(6):1721–1729CrossRefPubMedGoogle Scholar
  15. 15.
    Wang H, Liang D, King KF, Nagarsekar G, Chang Y, Ying L (2012) Improving GRAPPA using cross-sampled autocalibration data. Magn Reson Med 67(4):1042–1053CrossRefPubMedGoogle Scholar
  16. 16.
    Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13(4):600–612CrossRefPubMedGoogle Scholar
  17. 17.
    Wu C, Hu W, Kan R, Yu J, Sun X (2011) An improved GRAPPA image reconstruction algorithm for parallel MRI. In: Control and decision conference (CCDC), 2011 Chinese, pp 4096–4100Google Scholar

Copyright information

© CARS 2015

Authors and Affiliations

  • Santiago Aja-Fernández
    • 1
  • Daniel García Martín
    • 1
  • Antonio Tristán-Vega
    • 1
  • Gonzalo Vegas-Sánchez-Ferrero
    • 1
    • 2
  1. 1.Laboratorio de Procesado de Imagen (LPI), ETSI TelecomunicaciónUniversidad de ValladolidValladolidSpain
  2. 2.ACILBrigham and Women’s Hospital, Harvard Medical SchoolBostonUSA

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