On the Choice of Coil Combination Weights for Phase-Sensitive GRAPPA Reconstruction in Multichannel SWI

  • Sreekanth MadhusoodhananEmail author
  • Joseph Suresh Paul
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1022)


The feasibility of applying Generalized Auto-calibrating Partially Parallel Acquisition (GRAPPA) techniques has been established, with a two-fold or more reduction in scan time without compromising vascular contrast in Susceptibility Weighted Imaging (SWI) by choosing an optimal sensitivity map for combining the coil images. The overall SNR performance in GRAPPA is also dependent on the weights used for combining the GRAPPA reconstructed coil images. In this article, different methods for estimating the optimal coil combination weights are qualitatively and quantitatively analysed for maximizing the structural information in the tissue phase. The performance of various methods is visually evaluated using minimum Intensity Projection (mIP), Among the three methods, sensitivity estimated using the dominant eigenvector mentioned as ESPIRiT-based sensitivity in this article shows superior performance over the other two methods including estimating the sensitivity from the centre k-space line and from reconstructed channel images. Combining channel images using ESPIRiT sensitivity shows its ability to preserve the local phase variation and reduction in noise amplification.


Coil combination Parallel imaging Sensitivity SWI 



The authors are thankful to the Council of Scientific and Industrial Research-Senior Research Fellowship (CSIR-SRF, File No: 09/1208(0001)/2018.EMR-I) and planning board of Govt. of Kerala (GO(Rt) No. 101/2017/ITD.GOK(02/05/2017)), for financial assistance.


  1. 1.
    Haacke, E.M., Xu, Y., Cheng, Y.C., Reichenbach, J.R.: Susceptibility weighted imaging (SWI). Magn. Reson. Med. 52(3), 612–618 (2004)CrossRefGoogle Scholar
  2. 2.
    Wycliffe, N.D., Choe, J., Holshouser, B., Oyoyo, U.E., Haacke, E.M., Kido, D.K.: Reliability in detection of hemorrhage in acute stroke by a new three-dimensional gradient recalled echo susceptibility-weighted imaging technique compared to computed tomography: a retrospective study. J. Magn. Reson. Imaging 20(3), 372–377 (2004)CrossRefGoogle Scholar
  3. 3.
    Sehgal, V., Delproposto, Z., Haacke, E.M., Tong, K.A., Wycliffe, N., Kido, D.K., Xu, Y., Neelavalli, J., Haddar, D., Reichenbach, J.R.: Clinical applications of neuroimaging with susceptibility-weighted imaging. J. Magn. Reson. Imaging 22(4), 439–450 (2005)CrossRefGoogle Scholar
  4. 4.
    Haacke, E.M., Cheng, N.Y., House, M.J., Liu, Q., Neelavalli, J., Ogg, R.J., Khan, A., Ayaz, M., Kirsch, W., Obenaus, A.: Imaging iron stores in the brain using magnetic resonance imaging. Magn. Reson. Imaging 23(1), 1–25 (2005)CrossRefGoogle Scholar
  5. 5.
    Sehgal, V., Delproposto, Z., Haddar, D., Haacke, E.M., Sloan, A.E., Zamorano, L.J., Barger, G., Hu, J., Xu, Y., Prabhakaran, K.P., Elangovan, I.R.: Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J. Magn. Reson. Imaging 24(1), 41–51 (2006)CrossRefGoogle Scholar
  6. 6.
    Haacke, E., Makki, M.I., Selvan, M., Latif, Z., Garbern, J., Hu, J., Law, M., Ge, Y.: Susceptibility weighted imaging reveals unique information in multiple-sclerosis lesions using high-field MRI. In: Proceedings of International Society for Magnetic Resonance in Medicine, vol. 15, p. 2302 (2007)Google Scholar
  7. 7.
    Tong, K.A., Ashwal, S., Obenaus, A., Nickerson, J.P., Kido, D., Haacke, E.M.: Susceptibility-weighted MR imaging: a review of clinical applications in children. Am. J. Neuroradiol. 29(1), 9–17 (2008)CrossRefGoogle Scholar
  8. 8.
    Roh, K., Kang, H., Kim, I.: Clinical applications of neuroimaging with susceptibility weighted imaging. J. Korean Soc. Magn. Reson. Med. 18(4), 290–302 (2014)CrossRefGoogle Scholar
  9. 9.
    Haacke, E.M., Mittal, S., Wu, Z., Neelavalli, J., Cheng, Y.C.: Susceptibility-weighted imaging: technical aspects and clinical applications, part 1. Am. J. Neuroradiol. 30(1), 19–30 (2009)CrossRefGoogle Scholar
  10. 10.
    Wang, Y., Yu, Y., Li, D., Bae, K.T., Brown, J.J., Lin, W., Haacke, E.M.: Artery and vein separation using susceptibility-dependent phase in contrast-enhanced MRA. J. Magn. Reson. Imaging 12(5), 661–670 (2000)CrossRefGoogle Scholar
  11. 11.
    Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: sensitivity encoding for fast MRI. Magn. Reson. Med. 42(5), 952–962 (1999)CrossRefGoogle Scholar
  12. 12.
    Griswold, M.A., Jakob, P.M., Heidemann, R.M., Nittka, M., Jellus, V., Wang, J., Kiefer, B., Haase, A.: Generalized autocalibrating partially parallel acquisitions (GRAPPA). Magn. Reson. Med. 47(6), 1202–1210 (2002)CrossRefGoogle Scholar
  13. 13.
    Wang, Z., Wang, J., Detre, J.A.: Improved data reconstruction method for GRAPPA. Magn. Reson. Med. 54(3), 738–742 (2005)CrossRefGoogle Scholar
  14. 14.
    Lupo, J.M., Banerjee, S., Kelley, D., Xu, D., Vigneron, D.B., Majumdar, S., Nelson, S.J.: Partially-parallel, susceptibility-weighted MR imaging of brain vasculature at 7 Tesla using sensitivity encoding and an autocalibrating parallel technique. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS’06, pp. 747–750 (2006)Google Scholar
  15. 15.
    Roemer, P.B., Edelstein, W.A., Hayes, C.E., Souza, S.P., Mueller, O.M.: The NMR phased array. Magn. Reson. Med. 16(2), 192–225 (1990)CrossRefGoogle Scholar
  16. 16.
    Uecker, M., Lai, P., Murphy, M.J., Virtue, P., Elad, M., Pauly, J.M., Vasanawala, S.S., Lustig, M.: ESPIRiT—an eigenvalue approach to autocalibrating parallel MRI: where SENSE meets GRAPPA. Magn. Reson. Med. 71(3), 990–1001 (2014)CrossRefGoogle Scholar
  17. 17.
    Horn, R. A., Johnson, C. R.: Matrix analysis. Cambridge University Press (1985)Google Scholar
  18. 18.
    Abduljalil, A.M., Schmalbrock, P., Novak, V., Chakeres, D.W.: Enhanced gray and white matter contrast of phase susceptibility-weighted images in ultra-high-field magnetic resonance imaging. J. Magn. Reson. Imaging 18(3), 284–290 (2003)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Medical Image Computing and Signal Processing GroupIndian Institute of Information Technology and Management-Kerala (IIITM-K)TrivandrumIndia

Personalised recommendations