Motion-Robust Reconstruction Based on Simultaneous Multi-slice Registration for Diffusion-Weighted MRI of Moving Subjects

  • Bahram Marami
  • Benoit Scherrer
  • Onur Afacan
  • Simon K. Warfield
  • Ali Gholipour
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9902)


Simultaneous multi-slice (SMS) echo-planar imaging has had a huge impact on the acceleration and routine use of diffusion-weighted MRI (DWI) in neuroimaging studies in particular the human connectome project; but also holds the potential to facilitate DWI of moving subjects, as proposed by the new technique developed in this paper. We present a novel registration-based motion tracking technique that takes advantage of the multi-plane coverage of the anatomy by simultaneously acquired slices to enable robust reconstruction of neural microstructure from SMS DWI of moving subjects. Our technique constitutes three main components: (1) motion tracking and estimation using SMS registration, (2) detection and rejection of intra-slice motion, and (3) robust reconstruction. Quantitative results from 14 volunteer subject experiments and the analysis of motion-corrupted SMS DWI of 6 children indicate robust reconstruction in the presence of continuous motion and the potential to extend the use of SMS DWI in very challenging populations.


Simultaneous multi-slice Diffusion-weighted MRI Motion 


  1. 1.
    Behrens, T., Woolrich, M., Jenkinson, M., Johansen-Berg, H., Nunes, R., Clare, S., Matthews, P., Brady, J., Smith, S.: Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003)CrossRefGoogle Scholar
  2. 2.
    Scherrer, B., Schwartzman, A., Taquet, M., Sahin, M., Prabhu, S.P., Warfield, S.K.: Characterizing brain tissue by assessment of the distribution of anisotropic microstructural environments in diffusion-compartment imaging (DIAMOND). Magn. Reson. Med. 76, 963–977 (2015)CrossRefGoogle Scholar
  3. 3.
    Setsompop, K., Gagoski, B., Polimeni, J., Witzel, T., Wedeen, V., Wald, L.: Blipped-controlled aliasing in parallel imaging for simultaneous multislice echo planar imaging with reduced g-factor penalty. Magn. Reson. Med. 67, 1210–1224 (2012)CrossRefGoogle Scholar
  4. 4.
    Sotiropoulos, S.N., Jbabdi, S., Xu, J., Andersson, J.L., Moeller, S., Auerbach, E.J., Glasser, M.F., Hernandez, M., Sapiro, G., Jenkinson, M., et al.: Advances in diffusion MRI acquisition and processing in the human connectome project. Neuroimage 80, 125–143 (2013)CrossRefGoogle Scholar
  5. 5.
    Zaitsev, M., Maclaren, J., Herbst, M.: Motion artifacts in MRI: a complex problem with many partial solutions. J. Magn. Reson. Imaging 42(4), 887–901 (2015)CrossRefGoogle Scholar
  6. 6.
    Elhabian, S., Gur, Y., Vachet, C., Piven, J., Styner, M., Leppert, I.R., Pike, G.B., Gerig, G.: Subject-motion correction in HARDI acquisitions: choices and consequences. Front Neurol. 5, 240 (2014)CrossRefGoogle Scholar
  7. 7.
    Kober, T., Gruetter, R., Krueger, G.: Prospective and retrospective motion correction in diffusion magnetic resonance imaging of the human brain. Neuroimage 59(1), 389–398 (2012)CrossRefGoogle Scholar
  8. 8.
    Jiang, S., Xue, H., Counsell, S., Anjari, M., Allsop, J., Rutherford, M., Rueckert, D., Hajnal, J.: Diffusion tensor imaging of the brain in moving subjects: application to in-utero fetal and ex-utero studies. Magn. Reson. Med. 62, 645–655 (2009)CrossRefGoogle Scholar
  9. 9.
    Oubel, E., Koob, M., Studholme, C., Dietemann, J.L., Rousseau, F.: Reconstruction of scattered data in fetal diffusion MRI. Med. Image Anal. 16(1), 28–37 (2012)CrossRefGoogle Scholar
  10. 10.
    Fogtmann, M., Seshamani, S., Kroenke, C., Cheng, X., Chapman, T., Wilm, J., Rousseau, F., Studholme, C.: A unified approach to diffusion direction sensitive slice registration and 3-D DTI reconstruction from moving fetal brain anatomy. IEEE Trans. Med. Imaging 33(2), 272–289 (2014)CrossRefGoogle Scholar
  11. 11.
    Marami, B., Scherrer, B., Afacan, O., Erem, B., Warfield, S., Gholipour, A.: Motion-robust diffusion-weighted brain MRI reconstruction through slice-level registration-based motion tracking. IEEE Trans. Med. Imaging (2016, in press)Google Scholar
  12. 12.
    Agamennoni, G., Nieto, J., Nebot, E., et al.: Approximate inference in state-space models with heavy-tailed noise. IEEE Trans. Signal Process. 60(10), 5024–5037 (2012)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Li, Y., Shea, S.M., Lorenz, C.H., Jiang, H., Chou, M.C., Mori, S.: Image corruption detection in diffusion tensor imaging for post-processing and real-time monitoring. PLOS ONE 8(10), e49764 (2013)CrossRefGoogle Scholar
  14. 14.
    Powell, M.J.: The BOBYQA algorithm for bound constrained optimization without derivatives. Cambridge NA report NA2009/06, University of Cambridge (2009)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Bahram Marami
    • 1
  • Benoit Scherrer
    • 1
  • Onur Afacan
    • 1
  • Simon K. Warfield
    • 1
  • Ali Gholipour
    • 1
  1. 1.Boston Children’s HospitalHarvard Medical SchoolBostonUSA

Personalised recommendations