Coil Sensitivity Estimation for Optimal SNR Reconstruction and Intensity Inhomogeneity Correction in Phased Array MR Imaging

  • Prashanthi Vemuri
  • Eugene G. Kholmovski
  • Dennis L. Parker
  • Brian E. Chapman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3565)


Magnetic resonance (MR) images can be acquired by multiple receiver coil systems to improve signal-to-noise ratio (SNR) and to decrease acquisition time. The optimal SNR images can be reconstructed from the coil data when the coil sensitivities are known. In typical MR imaging studies, the information about coil sensitivity profiles is not available. In such cases the sum-of-squares (SoS) reconstruction algorithm is usually applied. The intensity of the SoS reconstructed image is modulated by a spatially variable function due to the non-uniformity of coil sensitivities. Additionally, the SoS images also have sub-optimal SNR and bias in image intensity. All these effects might introduce errors when quantitative analysis and/or tissue segmentation are performed on the SoS reconstructed images. In this paper, we present an iterative algorithm for coil sensitivity estimation and demonstrate its applicability for optimal SNR reconstruction and intensity inhomogeneity correction in phased array MR imaging.


Intensity Inhomogeneity Coil Sensitivity Coil Image Individual Coil Coil Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Roemer, P.B., Edelstein, W.A., Hayes, C.E., Souza, S.P., Mueller, O.M.: The NMR Phased-Array. Magnetic Resonance in Medicine 16, 192–225 (1990)CrossRefGoogle Scholar
  2. 2.
    Murakami, J.W., Hayes, C.E., Weinberger, E.: Intensity Correction of Phased-Array Sur-face Coil Images. Magnetic Resonance in Medicine 35, 585–590 (1996)CrossRefGoogle Scholar
  3. 3.
    Meyer, C.R., Bland, P.H., Pipe, J.: Retrospective Correction of Intensity Inhomogeneities in MRI. IEEE Transactions on Medical Imaging 14, 36–41 (1995)CrossRefGoogle Scholar
  4. 4.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A Nonparametric Method for Automatic Correc-tion of Intensity Non-uniformity in MRI data. IEEE Transactions on Medical Imaging 17, 87–97 (1998)CrossRefGoogle Scholar
  5. 5.
    Han, C., Hatsukami, T.S., Yuan, C.: A Multi-scale Method for Automatic Correction of In-tensity Non-Uniformity in MR Images. Journal of Magnetic Resonance Imaging 13, 428–436 (2001)CrossRefGoogle Scholar
  6. 6.
    Vokurka, E.A., Thacker, N.A., Jackson, A.: A Fast Model Independent Method for Auto-matic Correction of Intensity Non-uniformity in MRI Data. Journal of Magnetic Reso-nance Imaging 10, 550–562 (1999)CrossRefGoogle Scholar
  7. 7.
    Smythe, W.R.: Static and Dynamic Electricity. McGraw Hill, New York (1968)Google Scholar
  8. 8.
    Gudbjartsson, H., Patz, P.: The Rician Distribution of Noisy MRI Data. Magnetic Reso-nance in Medicine 34, 910–914 (1995)CrossRefGoogle Scholar
  9. 9.
    Ahn, C.B., Cho, Z.H.: A New Phase Correction Method in NMR Imaging Based on Auto-correlation and Histogram Analysis. IEEE Transactions on Medical Imaging 6, 32–36 (1987)CrossRefGoogle Scholar
  10. 10.
    Pruessmann, K.P., Weiger, M., Scheidegger, M.B., Boesiger, P.: SENSE: Sensitivity En-coding for Fast MRI. Magnetic Resonance in Medicine 42, 952–962 (1999)CrossRefGoogle Scholar
  11. 11.
    Liang, Z., MacFall, J.R., Harrington, D.P.: Parameter Estimation and Tissue Segmentation from Multispectral MR images. IEEE Transactions on Medical Imaging 13, 441–449 (1994)CrossRefGoogle Scholar
  12. 12.
    Wells, W.M., Grimson, W.E.L., Kikinis, R., Jolesz, F.A.: Adaptive Segmentation of MRI Data. IEEE Transactions on Medical Imaging 15, 429–442 (1996)CrossRefGoogle Scholar
  13. 13.
    Wilson, D.L., Noble, J.A.: An Adaptive Segmentation Algorithm of Time-of-flight MRA Data. IEEE Transactions on Medical Imaging 18, 938–945 (1999)CrossRefGoogle Scholar
  14. 14.
    Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press, New York (1996)Google Scholar
  15. 15.
    Kim, S.E., Kholmovski, E.G., Jeong, E.K., Buswell, H.R., Tsuruda, J.S., Parker, D.L.: Tri-ple Contrast Technique for Black Blood Imaging with Double Inversion Preparation. Mag-netic Resonance in Medicine 52, 1379–1387 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Prashanthi Vemuri
    • 1
  • Eugene G. Kholmovski
    • 1
  • Dennis L. Parker
    • 1
  • Brian E. Chapman
    • 2
  1. 1.UCAIR, Department of RadiologyUniversity of Utah, SLCUSA
  2. 2.Department of RadiologyUniversity of PittsburghUSA

Personalised recommendations