Groupwise Registration for Correcting Subject Motion and Eddy Current Distortions in Diffusion MRI Using a PCA Based Dissimilarity Metric

  • W. Huizinga
  • C. T. Metz
  • D. H. J. Poot
  • M. de Groot
  • W. J. Niessen
  • A. Leemans
  • S. Klein
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Before starting a diffusion-weighted MRI analysis, it is important to correct any misalignment between the diffusion-weighted images (DWIs) that was caused by subject motion and eddy current induced geometric distortions. Conventional methods adopt a pairwise registration approach, in which the non-DWI, a.k.a. the b = 0 image, is used as a reference. In this work, a groupwise affine registration framework, using a global dissimilarity metric, is proposed, which eliminates the need for selecting a reference image and which does not rely on a specific method that models the diffusion characteristics. The dissimilarity metric is based on principal component analysis (PCA) and is ideally suited for DWIs, in which the signal contrast varies drastically as a function of the applied gradient orientation. The proposed method is tested on synthetic data, with known ground-truth transformation parameters, and real diffusion MRI data, resulting in successful alignment.


Image registration Diffusion-weighted MRI Subject motion correction Principal component analysis 


  1. 1.
    Balci, S. et al.: Free-form B-Spline deformation model for groupwise registration. Proc. Stat. Regis. Workshop (MICCAI). 23–30 (2007)Google Scholar
  2. 2.
    Behrens, T.E.J., et al.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? NeuroImage 34, 144–155 (2007)CrossRefGoogle Scholar
  3. 3.
    de Geeter, N., et al.: A DTI-based model for TMS using the independent impedance method with frequency-dependent tissue parameters. Phys. Med. Biol. 57, 2169–2188 (2012)CrossRefGoogle Scholar
  4. 4.
    Fung, T.C.: Computation of the matrix exponential and its derivatives by scaling and squaring. Int. J. Numer. Methods Eng. 59, 1273–1286 (2004)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Jensen, H.J., et al.: Diffusional kurtosis imaging: the quantification of non-gaussian water diffusion by means of magnetic resonance imaging. Magn. Reson. Med. 53, 1432–1340 (2005)CrossRefGoogle Scholar
  6. 6.
    Jones, D.K., Leemans, A.: Diffusion tensor imaging. Methods Mol. Biol. 711, 127–144 (2011)CrossRefGoogle Scholar
  7. 7.
    Klein, S., et al.: Adaptive stochastic gradient descent optimization for image registration. Int. J. Comput. Vis. 81, 227–239 (2009)CrossRefGoogle Scholar
  8. 8.
    Klein, S., et al.: elastix: a toolbox for intensity based medical image registration. IEEE Trans. Med. imaging 29, 196–205 (2010)Google Scholar
  9. 9.
    Leemans, A., et al.: Mathematical framework for simulating diffusion tensor MR neural fiber bundles. Magn. Reson. Med. 53, 944–953 (2005)CrossRefGoogle Scholar
  10. 10.
    Leemans, A., et al.: Multiscale white matter fiber tract coregistration: a new feature-based approach to align diffusion tensor data. Magn. Reson. Med. 55, 1414–1423 (2006)CrossRefGoogle Scholar
  11. 11.
    Leemans, A., et al.: The B-matrix must be rotated when correcting for subject motion in DTI data. Magn. Reson. Med. 61, 1336–1349 (2009)CrossRefGoogle Scholar
  12. 12.
    Leemans, A., et al.: ExploreDTI: a graphical toolbox for processing, analyzing, and visualizing diffusion MR data. In: 17th Annual Meeting of International Society for Magnetic Resonance in Medicine, Hawaii, 2009, p. 3537Google Scholar
  13. 13.
    Marsland, S., et al.: A minimum description length objective function for groupwise non-rigid image registration. Image Vis. Comput. 26, 333–346 (2008)CrossRefGoogle Scholar
  14. 14.
    Melbourne, A., et al.: Registration of dynamic contrast-enhanced MRI using a progressive principal component registration (PPCR). Phys. Med. Biol. 52, 5147–5156 (2007)CrossRefGoogle Scholar
  15. 15.
    Metz, C.T., et al.: Nonrigid registration of dynamic medical imaging data using nD+t B-splines and a groupwise optimization approach. Med. Image Anal. 15, 238–249 (2010)CrossRefGoogle Scholar
  16. 16.
    Reijmer, Y.D., et al.: Improved sensitivity to cerebral white matter abnormalities in Alzheimer’s disease with spherical deconvolution based tractography. PLoS one. 7, 1371 (2012)CrossRefGoogle Scholar
  17. 17.
    Rohde, G.K., et al.: Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magn. Reson. Med. 51, 103–114 (2004)CrossRefGoogle Scholar
  18. 18.
    Schultz, T., Seidel, H.: Using eigenvalue derivatives for edge detection in DT-MRI data. In: Rigoll, G. (ed.) Pattern Recognition, vol. 5096, pp. 193–202. Springer, Berlin (2008)CrossRefGoogle Scholar
  19. 19.
    Sijbers, J., et al.: Parameter estimation from magnitude MR images. Int. J. Imaging Syst. Technol. 10, 109–114 (1999)CrossRefGoogle Scholar
  20. 20.
    Tournier, J.D., et al.: Diffusion tensor imaging and beyond. Magn. Reson. Med. 65, 1532–1556 (2011)CrossRefGoogle Scholar
  21. 21.
    van der Aa, N.P., et al..: Computation of eigenvalue and eigenvector derivatives for a general complex-valued eigensystem. Electron. J. Linear Algebra 16, 300–314 (2007)MATHMathSciNetGoogle Scholar
  22. 22.
    van der Aa, N.E., et al.: Does diffusion tensor imaging-based tractography at 3 months of age contribute to the prediction of motor outcome after perinatal arterial ischemic stroke? Stroke 42, 3410–3414 (2011)CrossRefGoogle Scholar
  23. 23.
    Wachinger, C., et al.: Simultaneous registration of multiple images: similarity metrics and efficient optimization. IEEE Trans. Pattern Anal. Mach. Intell. 7, 667–674 (2012)Google Scholar
  24. 24.
    Wang, H.C., et al.: Diffusion tensor imaging of vascular parkonsonism: structural changes in cerebral white matter and the association with clinical severity. Arch. Neurol. 69, 1340–1348 (2012)CrossRefGoogle Scholar
  25. 25.
    Wedeen, V.J., et al.: Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn. Reson. Med. 54, 1377–1386 (2005)CrossRefGoogle Scholar
  26. 26.
    Woods, P.: Characterizing volume and surface deformations in an atlas framework: theory, applications, and implementation. NeuroImage. 18, 769–788 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • W. Huizinga
    • 2
  • C. T. Metz
    • 2
  • D. H. J. Poot
    • 2
  • M. de Groot
    • 2
    • 3
  • W. J. Niessen
    • 1
    • 2
  • A. Leemans
    • 4
  • S. Klein
    • 2
  1. 1.Department of Applied Sciences, Imaging Science & TechnologyDelft University of TechnologyDelftThe Netherlands
  2. 2.Department of Radiology & Medical Informatics, Biomedical Imaging Group RotterdamErasmus MCRotterdamThe Netherlands
  3. 3.Department of EpidemiologyErasmus MCRotterdamThe Netherlands
  4. 4.Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands

Personalised recommendations