Accelerated High Spatial Resolution Diffusion-Weighted Imaging

  • Benoit Scherrer
  • Onur Afacan
  • Maxime Taquet
  • Sanjay P. Prabhu
  • Ali Gholipour
  • Simon K. Warfield
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9123)


Acquisition of a series of anisotropically oversampled acquisitions (so-called anisotropic “snapshots”) and reconstruction in the image space has recently been proposed to increase the spatial resolution in diffusion weighted imaging (DWI), providing a theoretical 8x acceleration at equal signal-to-noise ratio (SNR) compared to conventional dense k-space sampling. However, in most works, each DW image is reconstructed separately and the fact that the DW images constitute different views of the same anatomy is ignored. In addition, current approaches are limited by their inability to reconstruct a high resolution (HR) acquisition from snapshots with different subsets of diffusion gradients: an isotropic HR gradient image cannot be reconstructed if one of its anisotropic snapshots is missing, for example due to intra-scan motion, even if other snapshots for this gradient were successfully acquired. In this work, we propose a novel multi-snapshot DWI reconstruction technique that simultaneously achieves HR reconstruction and local tissue model estimation while enabling reconstruction from snapshots containing different subsets of diffusion gradients, providing increased robustness to patient motion and potential for acceleration. Our approach is formalized as a joint probabilistic model with missing observations, from which interactions between missing snapshots, HR reconstruction and a generic tissue model naturally emerge. We evaluate our approach with synthetic simulations, simulated multi-snapshot scenario and in vivo multi-snapshot imaging. We show that (1) our combined approach ultimately provides both better HR reconstruction and better tissue model estimation and (2) the error in the case of missing snapshots can be quantified. Our novel multi-snapshot technique will enable improved high spatial characterization of the brain connectivity and microstructure in vivo.


Diffusion-weighted imaging High spatial resolution Model-based Joint model 


  1. 1.
    Bach, M., Fritzsche, K.H., Stieltjes, B., Laun, F.B.: Investigation of resolution effects using a specialized diffusion tensor phantom. Magn Reson Med 71, 1108–1116 (2013)CrossRefGoogle Scholar
  2. 2.
    Efron, B.: Estimating the error rate of a prediction rule: improvement on cross-validation. J. Am. Stat. Assoc. 78(382), 316–331 (1983)zbMATHMathSciNetCrossRefGoogle Scholar
  3. 3.
    Gudbjartsson, H., Patz, S.: The Rician distribution of noisy MRI data. Magn Reson Med 34(6), 910–914 (1995)CrossRefGoogle Scholar
  4. 4.
    Holdsworth, S.J., Skare, S., Newbould, R.D., Guzmann, R., Blevins, N.H., Bammer, R.: Readout-segmented EPI for rapid high resolution diffusion imaging at 3T. Eur J Radiol 65(1), 36–46 (2008)CrossRefGoogle Scholar
  5. 5.
    Mills, R.: Self-diffusion in normal and heavy water in the range 1–45.deg. J. Phys. Chem. 77(5), 685–688 (1973)CrossRefGoogle Scholar
  6. 6.
    Poot, D.H., Jeurissen, B., Bastiaensen, Y., Veraart, J., Van Hecke, W., Parizel, P.M., Sijbers, J.: Super-resolution for multislice diffusion tensor imaging. Magn Reson Med 69(1), 103–113 (2013)CrossRefGoogle Scholar
  7. 7.
    Powell, M.J.D.: The BOBYQA algorithm for bound constrained optimization without derivatives. In: Technical report NA2009/06. Department of Applied Mathematics and Theoretical Physics, Cambridge, England (2009)Google Scholar
  8. 8.
    Scherrer, B., Gholipour, A., Warfield, S.K.: Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med Imag Anal. 16(7), 1465–1476 (2012)CrossRefGoogle Scholar
  9. 9.
    Scherrer, B., Warfield, S.K.: Parametric representation of multiple white matter fascicles from cube and sphere diffusion MRI. PLoS ONE 7(11), e48232 (2012)CrossRefGoogle Scholar
  10. 10.
    Song, A.W., Chang, H.C., Petty, C., Guidon, A., Chen, N.K.: Improved delineation of short cortical association fibers and gray/white matter boundary using whole-brain three-dimensional diffusion tensor imaging at submillimeter spatial resolution. Brain Connect 4(9), 636–640 (2014)CrossRefGoogle Scholar
  11. 11.
    Sotiropoulos, S.N., Jbabdi, S., Xu, J., Andersson, J.L., Moeller, S., Auerbach, E.J., Glasser, M.F., Hernandez, M., Sapiro, G., Jenkinson, M., Feinberg, D.A., Yacoub, E., Lenglet, C., Van Essen, D.C., Ugurbil, K., Behrens, T.E.: WU-Minn HCP Consortium: advances in diffusion MRI acquisition and processing in the human connectome project. Neuroimage 80, 125–143 (2013)CrossRefGoogle Scholar
  12. 12.
    Tobisch, A., Neher, P., Rowe, M., Maier-Hein, K., Zhang, H.: Model-based super-resolution of diffusion MRI. In: Schultz, T., Nedjati-Gilani, G., Venkataraman, A., O’Donnell, L., Panagiotaki, E. (eds.) Computational Diffusion MRI and Brain Connectivity Workshop, pp. 25–34. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  13. 13.
    Tuch, D.S., Reese, T.G., Wiegell, M.R., Makris, N., Belliveau, J.W., Wedeen, V.J.: High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48(4), 577–582 (2002)CrossRefGoogle Scholar
  14. 14.
    Vos, S.B., Jones, D.K., Viergever, M.A., Leemans, A.: Partial volume effect as a hidden covariate in DTI analyses. Neuroimage 55(4), 1566–1576 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Benoit Scherrer
    • 1
  • Onur Afacan
    • 1
  • Maxime Taquet
    • 1
  • Sanjay P. Prabhu
    • 1
  • Ali Gholipour
    • 1
  • Simon K. Warfield
    • 1
  1. 1.Department of Radiology Boston Children’s HospitalComputational Radiology LaboratoryBostonUSA

Personalised recommendations