Skip to main content

Advertisement

Log in

Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach

  • ORIGINAL ARTICLE
  • Published:
Medical & Biological Engineering & Computing Aims and scope Submit manuscript

Abstract

The recent challenge in high angular resolution diffusion imaging (HARDI) is to find a tractography process that provides information about the neural architecture within the white matter of the brain in a clinically feasible measurement time. The great success of the HARDI technique comes from its capability to overcome the problem of crossing fiber detection. However, it requires a large number of diffusion-weighted (DW) images which is problematic for clinical time and hardware. The main contribution of this paper is to develop a full tractography framework that gives an accurate estimate of the crossing fiber problem with the aim of reducing data acquisition time. We explore the interpolation in the gradient direction domain as a method to estimate the HARDI signal from a reduced set of DW images. The experimentation was performed in a first time on simulated data for a quantitative evaluation using the Tractometer system. We used, also, in vivo human brain data to demonstrate the potential of our pipeline. Results on both simulated and real data illustrate the effectiveness of our approach to perform the brain connectivity. Overall, we have shown that the proposed approach achieves competitive results to other tractography methods according to Tractometer connectivity metrics.

Graphical Abstract

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

Notes

  1. http://www.tractometer.org/ismrm_2015_challenge/

  2. http://www.tractometer.org/

References

  1. Mori S, Crain B, Chacko V, Zijl M V (1999) Three dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:265–269

    Article  CAS  PubMed  Google Scholar 

  2. Basser P, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DTMRI data. Magn Reson Med 44:625–632

    Article  CAS  PubMed  Google Scholar 

  3. Golby A, Kindlmann G, Norton I, Yarmarkovich A, Pieper S, Kikinis R (2011) Interactive diffusion tensor tractography visualization for neurosurgical planning. Neurosurgery 68:469–505

    Article  Google Scholar 

  4. Clark C, Barrick T, Murphy M, Bell B (2003) White matter fiber tracking in patients with space-occupying lesions of the brain: A new technique for neurosurgical planning. NeuroImage 20:1601–1608

    Article  PubMed  Google Scholar 

  5. Mesaros S, Rocca M, Kacar K, Kostic J, Copetti M, Stosic-Opincal T, Filippi M (2012) Diffusion tensor MRI tractography and cognitive impairment in multiple sclerosis. Neurology 36:969–975

    Article  Google Scholar 

  6. Wang Y, Sun P, Wang Q, Trinkaus K, Schmidt R, Naismith R, Anne H Differentiation and quantification of inflammation, demyelination and axon injury or loss in multiple sclerosis, Brain Advance Access published February 26

  7. Morikawa M, Kiuchi K, Taoka T, Nagauchi K, Kichikawa K, Kishimoto T (2010) Uncinate fasciculus-correlated cognition in Alzheimer’s disease: a diffusion tensor imaging study by tractography. Psychogeriatrics 1:15–20

    Article  Google Scholar 

  8. Voineskos A, Lobaugh N, Bouix S, Rajji T, Miranda D, Kennedy J, Shenton M (2010) Diffusion tensor tractography findings in schizophrenia across the adult lifespan. Brain 5:1494–1504

    Article  Google Scholar 

  9. Hagmann P, Jonasson L, Maeder P, Thiran J, Wedeen V, Meuli R (2006) understanding diffusion mr imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. RadioGraphics 26:205–235

    Article  Google Scholar 

  10. Mori S (2007) Introduction to diffusion tensor imaging

  11. Basser P, Mattiello J, Lebihan D (1994) Mr diffusion tensor spectroscopy and imaging. Biophys J 66:259–267

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Jeurissen B (2012) Improved analysis of brain connectivity using high angular resolution diffusion MRI, PhD thesis

  13. Sotiropoulos S, Jbabdi S, Xu J, Andersson J, Moeller S, Auerbach E, Glasser M, Hernandez M, Sapiro G, Jenkinson M, Feinberg D, Yacoub E, Lenglet C, Essen D V, Ugurbil K, Behrens T (2013) Advances in diffusion MRI acquisition and processing in the Human Connectome Project. Neuroimage 80:125–143

    Article  PubMed  PubMed Central  Google Scholar 

  14. Calabrese E, Badea A, Coe C, Lubach G, Styner M, Johnson G (2014) Investigating the tradeoffs between spatial resolution and diffusion sampling for brain mapping with diffusion tractography: Timewellspent. Human BrainMapping 2014:40–86

    Google Scholar 

  15. Tuch D, Belliveau J, Reese T, Wedeen V (1999) High angular resolution imaging of the human brain. In: Proceedings of the international society for the magnetic resonance in medicine, pp 321

  16. Tuch D (2004) Q-ball imaging. Magn Reson Med 52:1358–1372

    Article  PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  18. Tournier J, Calamante F, Gadian D, Connelly A (2004) Direct estimation of the fiber orientation density function from diffusion- weighted MRI data using spherical deconvolution. Neuro-Image 23:1176–1185

    PubMed  Google Scholar 

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

    Article  PubMed  Google Scholar 

  20. Tournier J, Calamante F, Connelly A (2009) High How many diffusion gradient directions are required for HARDI? Proc ISMRM, pp 358

  21. Michailovich O, Rathi Y (2010) Fast and accurate reconstruction of HARDI data using compressed sensing, Medical image computing and computer assisted intervention, Springer, pp 607–614

  22. Tristan-Vega A, Westin C (2011) Probabilistic ODF estimation from reduced HARDI data with sparse regularization, Medical image computing and computer assisted intervention, Springer, pp 182–190

  23. Daducci A, Ville D V D, Thiran J, Wiaux Y (2014) Sparse regularization for fiber ODF reconstruction: from the suboptimality of l2 and l1 priors to l0. Med Image Anal 6:820–833

    Article  Google Scholar 

  24. Sepehrband F, Choupan J, Caruyer E, Kurniawan N, Gal Y Lop- DWI: A novel scheme for pre-processing of diffusion weighted images in the gradient direction domain, Frontiers in Neurology 5

  25. Bates A, Khalid Z, Kennedy R (2016) On the use of antipodal optimal dimensionality sampling scheme on the sphere for recovering intra-voxel fibre structure in diffusion MRI. In: Fuster A, Ghosh A, Kaden E, Rathi Y, Reisert M (eds) Computational diffusion MRI mathematics and visualization. Springer, Cham

  26. Cho K, Yeh C, Chao Y, Chen J W J, Lin C (2009) Potential in reducing scan times of HARDI by accurate correction of the cross-term in a hemispherical encoding scheme. J Magn Reson Imaging 29:1386–1394

    Article  PubMed  Google Scholar 

  27. Tao X, Miller J (2006) A method for registering diffusion weighted magnetic resonance images. Proc MICCAI 4191:594–602

    Google Scholar 

  28. Coup P, Manjn J, Chamberland M, Descoteaux M, Hiba B (2013) Collaborative patchbased super-resolution for diffusion-weighted images. Neuroimage 83:245–261

    Article  Google Scholar 

  29. Dyrby T, Lundell H, Burke M, Reislev N, Paulsona M P O B, Siebner H (2014) Interpolation of diffusion weighted imaging datasets. Neuroimage 103:202–213

    Article  PubMed  Google Scholar 

  30. Yap P, An H, Chen Y, Shen D (2014) Fiber-driven resolution enhancement of diffusion-weighted images. Neuroimage 84:939–950

    Article  PubMed  Google Scholar 

  31. Carfora M (2007) Interpolation on spherical geodesic grids: a comparative study. J Comput Appl Math 210:99–105

    Article  Google Scholar 

  32. BenAlaya I, Jribi M, Ghorbel F, SappeyMarinier D, Kraiem T (2017) Fast and accurate estimation of the HARDI signal in diffusion MRI using a nearest-neighbor interpolation approach. Innovation and Research in BioMedical engineering (IRBM) 38:156–166

    Google Scholar 

  33. Cheng J, Yap P, Shen D (2014) Single and multiple shell sampling design in dMRI using spherical code and mixed integer linear programming, ISMRM, pp 2558

  34. Descoteaux M, Wiest-Daessl N, Prima S, Barillot C, Deriche R (2008) Impact of Rician adapted Non-Local Means filtering on HARDI. Medical image computing and computer-assisted intervention: MICCAI 11:122–301

    PubMed  Google Scholar 

  35. BenAlaya I, Jribi M, Ghorbel F, Kraiem T (2016) A novel geometrical approach for a rapid estimation of the HARDI signal in diffusion MRI. International Conference on Image and Signal Processing-ICISP 9680:253–261

    Article  Google Scholar 

  36. Tournier J, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66

    Article  Google Scholar 

  37. Descoteaux M, Deriche R, Knsche T, Anwander A (2009) Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans Med Imaging 28:269–286

    Article  PubMed  Google Scholar 

  38. Berman J, Chung S, Mukherjee P, Hess CP, Han E, Henry R (2008) Probabilistic streamline q-ball tractography using the residual bootstrap. Neuroimage 39:625–632

    Article  Google Scholar 

  39. Neher P, Laun F, Stieltjes B, Maier-Hein K (2014) Fiberfox: facilitating the creation of realistic white matter software phantoms. MRM 72:1460–1470

    Article  PubMed  Google Scholar 

  40. Maier-Hein K, Neher P, Houde C, Ct M, Garyfallidis E et al The challenge of mapping the human connectome based on diffusion tractography, Nature Communications, Nature Publishing Group 1

  41. Ct M, Girard G, Bor A, Garyfallidis E, Houde J, Descoteaux M (2013) Tractometer: Towards validation of tractography pipelines. Med Image Anal 17:857–844

    Google Scholar 

  42. Tuch D, Reese T, Wiegell M, Makris N, Belliveau J, Wedeen V (2002) High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48:577–582

    Article  PubMed  Google Scholar 

  43. Tournier J, Calamante F, Connelly A (2013) Determination of the appropriate b value and number of gradient directions for high angular resolution diffusion-weighted imaging. NMR in Biomedecine 26:1775–1786

    Article  Google Scholar 

  44. Zhang H, Gao Z, Xu L, Yu X, Wong K, Liu H, Zhuang L, Shi P (2018) A meshfree representation for cardiac medical image computing. IEEE J Transl Eng Health Med 6:1800212

    PubMed  Google Scholar 

  45. Wang X, Chen T, Zhang S, Schaerer J, Qian Z, Huh S, Metaxas D, Axel L (2015) Meshless deformable models for 3D cardiac motion and strain analysis from tagged MRI. Magn Reson Imaging 33:146–160

    Article  PubMed  Google Scholar 

  46. Mori S, van Zijl PC (2002) Fiber tracking: principles and strategies - a technical review. NMR Biomed 15:468–480

    Article  PubMed  Google Scholar 

  47. Wedeen V J, Wang R P, Schmahmann J D, Benner T, Tseng W Y I, Dai G, de Crespigny AJ (2008) Diffusion spectrum magnetic resonance imaging (DSI) tractography of crossing fibers. Neuroimage 41:1267–1277

    Article  CAS  PubMed  Google Scholar 

  48. Jäger J, Klein A, Buhmann M, Skrandies W (2016) Reconstruction of electroencephalographic data using radial basis functions. Clin Neurophysiol 2016:1978–1983

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to express their sincere thanks to Pr. Jean-Christophe Houde for his help and for many fruitful discussions.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Majdi Jribi.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Alaya, I.B., Jribi, M., Ghorbel, F. et al. Quantitative evaluation of fiber tractography with a Delaunay triangulation–based interpolation approach. Med Biol Eng Comput 57, 925–938 (2019). https://doi.org/10.1007/s11517-018-1932-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-018-1932-y

Keywords

Navigation