Skip to main content

Advertisement

Log in

Reproducibility and consistency of evaluation techniques for HARDI data

  • Research Article
  • Published:
Magnetic Resonance Materials in Physics, Biology and Medicine Aims and scope Submit manuscript

Abstract

Object

The reproducibility of three evaluation techniques for high angular resolution diffusion imaging (HARDI) data, the diffusion tensor model, q-ball reconstruction and spherical deconvolution, are compared.

Materials and methods

Two healthy volunteers were measured in a 3 T MR system six times with the same measurement parameters; one subject was measured with different b-values. The data was evaluated to compare the consistency and reproducibility of reconstructed diffusion directions and anisotropy values for the three investigated diffusion evaluation techniques. The angle difference between the reconstructed main directions of diffusion for the investigated techniques was evaluated. For q-ball and spherical deconvolution the consistency of the second dominant diffusion direction was additionally examined.

Results

The differences between the tensor model and q-ball or spherical deconvolution in the estimated diffusion direction decrease with an increase in fractional anisotropy. Increasing the smoothing kernel in q-ball reconstruction renders the results similar to the ones from the diffusion tensor evaluation. Consistency in the reconstructed directions did increase for larger b-values.

Conclusion

The evaluation of HARDI data in clinical conditions with q-ball or spherical deconvolution shows consistency and reproducibility similar to the diffusion tensor model, but provides valuable additional information about a second dominant direction of diffusion.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Behrens TE, Berg HJ, Jbabdi S, Rushworth MF, Woolrich MW (2007) Probabilistic diffusion tractography with multiple fibre orientations: what can we gain?. Neuroimage 34(1): 144–155

    Article  CAS  PubMed  Google Scholar 

  2. Mori S, Barker PB (1999) Diffusion magnetic resonance imaging: its principle and applications. New Anat 257: 102–109

    Article  CAS  Google Scholar 

  3. Mori S, van Zijl PC (2002) Fiber tracking: principles and strategies—a technical review. NMR Biomed 2002(7–8): 468–480

    Article  Google Scholar 

  4. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci 96(18): 10422–10427

    Article  CAS  PubMed  Google Scholar 

  5. Seqhier ML, Lazeyras F, Zimine S, Maier SE, Hanquinet S, Delavelle J, Volpe JJ, Huppi PS (2004) Combination of event-related fMRI and diffusion tensor imaging in an infant with perinatal stroke. Neuroimage 21(1): 463–472

    Article  Google Scholar 

  6. Basser PJ, Mattiello J, LeBihan D (1994) Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson 103(1): 247–254

    CAS  Google Scholar 

  7. Tuch DS (2004) Q-ball imaging. Mag Res Med 52(6): 1358–1372

    Article  Google Scholar 

  8. Hess PCh, Mukherjee P, Han ET, Xu D, Vigneron DB (2006) Q-ball reconstruction of multimodal fiber orientations using the spherical harmonic basis. Mag Res Med 56(1): 104–117

    Article  Google Scholar 

  9. Descoteaux M, Angelino E, Fitzgibbons S, Deriche R (2007) Regularized, fast, and robust analytical Q-ball imaging. Magn Reson Med 58(3): 497–510

    Article  PubMed  Google Scholar 

  10. Parker GJ, Alexander DC (2005) Probabilistic anatomical connectivity derived from the microscopic persistent angular structure of cerebral tissue. Philos Trans R Soc Lond B 360(1457): 893–902

    Article  Google Scholar 

  11. Özarslan E, Shepherd TM, Vemuri BC, Blackband SJ, Mareci TH. (2006) Resolution of complex tissue microarchitecture using the diffusion orientation transform (DOT). Neuroimage 31(3): 1086–1103

    Article  PubMed  Google Scholar 

  12. Tournier JD, Calamante F, Gadian DG, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion-weighted MRI data using spherical deconvolution. Neuroimage 23(3): 1176–1185

    Article  PubMed  Google Scholar 

  13. Anderson AW (2005) Measurement of fiber orientation distributions using high angular resolution diffusion imaging. Magn Reson Med 54(5): 1194–1206

    Article  PubMed  Google Scholar 

  14. Özarslan E, Mareci TH (2003) Generalized diffusion tensor imaging and analytical relationships between diffusion tensor imaging and high angular resolution diffusion imaging. Mag Res Med 50(5): 955–965

    Article  Google Scholar 

  15. Perrin M, Poupon C, Rieul B, Leroux P, Constantinesco A, Mangin JF, LeBihan D (2005) Validation of q-ball imaging with a diffusion fiber-crossing phantom on a clinical scanner. Philos Trans R Soc B 360: 881–891

    Article  Google Scholar 

  16. Batchelor PG, Atkinson D, Hill DLG, Calamante F, Connelly A (2003) Anisotropic noise propagation in diffusion tensor imaging MRI sampling schemes. Mag Res Med 49(6): 1143–1151

    Article  CAS  Google Scholar 

  17. Jones DK (2004) The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a monte carlo study. Mag Res Med 51(4): 807–815

    Article  Google Scholar 

  18. Reese TG, Heid O, Weisskoff RM, Wedeen VJ (2003) Reduction of eddy-current-induced distortion in diffusion MRI using a twice-refocused spin echo. Mag Res Med 49(1): 177–182

    Article  CAS  Google Scholar 

  19. Tournier JD, Calamante F, Connelly A (2007) Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution. Neuroimage 35(4): 1459–1472

    Article  PubMed  Google Scholar 

  20. Jones DK (2003) Determining and visualising uncertainty in estimates of fiber orientation from diffusion tensor MRI. Mag Res Med 49(1): 7–12

    Article  Google Scholar 

  21. Jian B, Vemuri BC (2007) A unified computational framework for deconvolution to reconstruct multiple fibers from diffusion weighted MRI. IEEE Trans Med Imaging 26(11): 1464–1471

    Article  PubMed  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kamil Gorczewski.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gorczewski, K., Mang, S. & Klose, U. Reproducibility and consistency of evaluation techniques for HARDI data. Magn Reson Mater Phy 22, 63–70 (2009). https://doi.org/10.1007/s10334-008-0144-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10334-008-0144-0

Keywords

Navigation