FOD-Based Registration for Susceptibility Distortion Correction in Connectome Imaging

  • Yuchuan Qiao
  • Wei Sun
  • Yonggang ShiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11083)


Multi-shell, high resolution diffusion MRI (dMRI) data from the Human Connectome Project (HCP) provides an unprecedented opportunity for the in vivo mapping of human brain pathways. It was recently noted, however, that significant distortions remain present in the data of most subjects preprocessed by the HCP-Pipeline, which have been widely distributed and used extensively in connectomics research. Fundamentally this is caused by the reliance of the HCP tools on the B0 images for registering data from different phase encodings (PEs). In this work, we develop an improved framework to remove the residual distortion in data generated by the HCP-Pipeline. Our method is based on more advanced registration of fiber orientation distribution (FOD) images, which represent information of dMRI scans from all gradient directions and thus provide more reliable contrast to align data from different PEs. In our experiments, we focus on the brainstem area and compare our method with the preprocessing steps in the HCP-Pipeline. We show that our method can provide much improved distortion correction and generate FOD images with more faithful representation of brain pathways.


  1. 1.
    Essen, D.V., Ugurbil, K., et al.: The Human Connectome Project: a data acquisition perspective. NeuroImage 62(4), 2222–2231 (2012)CrossRefGoogle Scholar
  2. 2.
    Glasser, M.F., et al.: The minimal preprocessing pipelines for the Human Connectome Project. NeuroImage 80, 105–124 (2013)CrossRefGoogle Scholar
  3. 3.
    Tang, Y., Sun, W., Toga, A.W., Ringman, J.M., Shi, Y.: A probabilistic atlas of human brainstem pathways based on connectome imaging data. NeuroImage 169, 227–239 (2018)CrossRefGoogle Scholar
  4. 4.
    Jezzard, P., Balaban, R.S.: Correction for geometric distortion in echo planar images from B0 field variations. Magn. Reson. Med. 34(1), 65–73 (1995)CrossRefGoogle Scholar
  5. 5.
    Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20(2), 870–888 (2003)CrossRefGoogle Scholar
  6. 6.
    Holland, D., Kuperman, J.M., Dale, A.M.: Efficient correction of inhomogeneous static magnetic field-induced distortion in echo planar imaging. NeuroImage 50(1), 175–183 (2010)CrossRefGoogle Scholar
  7. 7.
    Irfanoglu, M.O., Modi, P., Nayak, A., Hutchinson, E.B., Sarlls, J., Pierpaoli, C.: DR-BUDDI (Diffeomorphic Registration for Blip-Up blip-Down Diffusion Imaging) method for correcting echo planar imaging distortions. NeuroImage 106, 284–299 (2015)CrossRefGoogle Scholar
  8. 8.
    Tran, G., Shi, Y.: Fiber orientation and compartment parameter estimation from multi-shell diffusion imaging. IEEE Trans. Med. Imag. 34(11), 2320–2332 (2015)CrossRefGoogle Scholar
  9. 9.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  10. 10.
    Qiao, Y., van Lew, B., Lelieveldt, B.P., Staring, M.: Fast automatic step size estimation for gradient descent optimization of image registration. IEEE Trans. Med. Imaging 35(2), 391–403 (2016)CrossRefGoogle Scholar
  11. 11.
    Braak, H., Thal, D.R., Ghebremedhin, E., Del Tredici, K.: Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J. Neuropathol. Exp. Neurol. 70(11), 960–969 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern CaliforniaLos AngelesUSA

Personalised recommendations