A Variational Image-Based Approach to the Correction of Susceptibility Artifacts in the Alignment of Diffusion Weighted and Structural MRI

  • Ran Tao
  • P. Thomas Fletcher
  • Samuel Gerber
  • Ross T. Whitaker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5636)


This paper presents a method for correcting the geometric and greyscale distortions in diffusion-weighted MRI that result from inhomogeneities in the static magnetic field. These inhomogeneities may due to imperfections in the magnet or to spatial variations in the magnetic susceptibility of the object being imaged—so called susceptibility artifacts. Echo-planar imaging (EPI), used in virtually all diffusion weighted acquisition protocols, assumes a homogeneous static field, which generally does not hold for head MRI. The resulting distortions are significant, sometimes more than ten millimeters. These artifacts impede accurate alignment of diffusion images with structural MRI, and are generally considered an obstacle to the joint analysis of connectivity and structure in head MRI. In principle, susceptibility artifacts can be corrected by acquiring (and applying) a field map. However, as shown in the literature and demonstrated in this paper, field map corrections of susceptibility artifacts are not entirely accurate and reliable, and thus field maps do not produce reliable alignment of EPIs with corresponding structural images. This paper presents a new, image-based method for correcting susceptibility artifacts. The method relies on a variational formulation of the match between an EPI baseline image and a corresponding T2-weighted structural image but also specifically accounts for the physics of susceptibility artifacts. We derive a set of partial differential equations associated with the optimization, describe the numerical methods for solving these equations, and present results that demonstrate the effectiveness of the proposed method compared with field-map correction.


Mutual Information Statistical Parametric Mapping Geometric Distortion Susceptibility Artifact Phantom 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.
    Jezzard, P., Balaban, R.: Correction for geometric distortion in echo planar images from B0 field variations. Magnetic Resonance in Medicine 34, 65–73 (1995)CrossRefGoogle Scholar
  2. 2.
    Wu, M., Chang, L.C., Walker, L., Lemaitre, H., Barnett, A.S., Marenco, S., Pierpaloi, C.: Comparison of EPI distortion correction methods in diffusion tensor mri using a novel framework. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part II. LNCS, vol. 5242, pp. 321–329. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  3. 3.
    Studholme, C., Constable, R., Duncan, J.: Accurate alignment of functional epi data to anatomical mri using a physics based distortion model. IEEE Trans. Med. Imaging, 1115–1127 (2000)Google Scholar
  4. 4.
    Tao, G., He, R., Poonawalla, A.H., Narayana, P.: The correction of epi-induced geometric distortions and their evaluation. In: Proc. ICIP (2007)Google Scholar
  5. 5.
    Kybic, J., Nirkko, A., Unser, M.: Unwarping of unidirectionally distorted epi images. IEEE Trans. on Medical Imaging 19, 80–93 (2000)CrossRefGoogle Scholar
  6. 6.
    Hellier, P., Barillot, C.: Multimodal non-rigid warping for correction of distortions in functional MRI. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 512–520. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  7. 7.
    Tai, X.C., Lie, K.A., Chan, T., Osher, S.: Image Processing Based on Partial Differential Equations. Springer, Heidelberg (2007)CrossRefzbMATHGoogle Scholar
  8. 8.
    Leemput, K.V., Maes, F., Vandermeulen, D., Suetens, P.: Automated model-based tissue classification of MR images of the brain. IEEE Trans. on Medical Imaging 18(10), 897–908 (1999)CrossRefGoogle Scholar
  9. 9.
    Rohde, G., Barnett, A., Basser, P., Marenco, S., Pierpaoli, C.: Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magnetic Resonance in Medicine 51, 103–114 (2004)CrossRefGoogle Scholar
  10. 10.
    Andersson, J., Hutton, C., Ashburner, J., Turner, R., Friston, K.: Modelling geometric deformations in epi time series. NeuroImage 13, 903–919 (2001)CrossRefGoogle Scholar
  11. 11.
    Hutton, C., Bork, A., Josephs, O., Deichmann, R., Ashburner, J., Turner, R.: Image distortion correction in fmri: A quantitative evaluation. NeuroImage 16, 217–240Google Scholar
  12. 12.
    Jenkinson, M.: Fast, automated, N-dimensional phase-unwrapping algorithm. Magnetic Resonance in Medicine 49, 193–197Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Ran Tao
    • 1
  • P. Thomas Fletcher
    • 1
  • Samuel Gerber
    • 1
  • Ross T. Whitaker
    • 1
  1. 1.School of Computing, University of Utah, Scientific Computing and Imaging Institute, University of UtahUSA

Personalised recommendations