Journal of Digital Imaging

, Volume 24, Issue 2, pp 339–351 | Cite as

Performing Real-Time Interactive Fiber Tracking

  • Adiel Mittmann
  • Tiago H. C. Nobrega
  • Eros Comunello
  • Juliano P. O. Pinto
  • Paulo R. Dellani
  • Peter Stoeter
  • Aldo von Wangenheim


Fiber tracking is a technique that, based on a diffusion tensor magnetic resonance imaging dataset, locates the fiber bundles in the human brain. Because it is a computationally expensive process, the interactivity of current fiber tracking tools is limited. We propose a new approach, which we termed real-time interactive fiber tracking, which aims at providing a rich and intuitive environment for the neuroradiologist. In this approach, fiber tracking is executed automatically every time the user acts upon the application. Particularly, when the volume of interest from which fiber trajectories are calculated is moved on the screen, fiber tracking is executed, even while it is being moved. We present our fiber tracking tool, which implements the real-time fiber tracking concept by using the video card’s graphics processing units to execute the fiber tracking algorithm. Results show that real-time interactive fiber tracking is feasible on computers equipped with common, low-cost video cards.

Key words

Fiber tracking diffusion tensor imaging graphics processing units real-time applications 



The authors would like to thank Dr. Antonio Carlos dos Santos, from the Faculty of Medicine at Ribeirão Preto, for his help evaluating the fiber tracking tool.

Supplementary material

10278_2009_9266_MOESM1_ESM.avi (29.3 mb)
ESM (AVI 29.2 MB)


  1. 1.
    Basser PJ, Mattiello J, Le Bihan D: MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267, 1994PubMedCrossRefGoogle Scholar
  2. 2.
    Basser PJ, Mattiello J, Le Bihan, D: Estimation of the effective self-diffusion tensor from the NMR spin echo. J Magn Reson B 103:247–254, 1994.PubMedCrossRefGoogle Scholar
  3. 3.
    Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H: Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546, 2001.PubMedCrossRefGoogle Scholar
  4. 4.
    Yamada K, Shiga K, Kizu O, Ito H, Akiyama K, Nakagawa M, Nishimura T: Oculomotor nerve palsy evaluated by diffusion-tensor tractography. Neuroradiology 48:434–437, 2006.PubMedCrossRefGoogle Scholar
  5. 5.
    Beaulieu C: The basis of anisotropic water diffusion in the nervous system—a technical review. NMR Biomed, 15:435–455, 2002.PubMedCrossRefGoogle Scholar
  6. 6.
    Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME: Tracking neuronal fiber pathways in the living human brain. Proc. Natl. Acad. Sci. USA 96, 10422–10427, 1999.PubMedCrossRefGoogle Scholar
  7. 7.
    Basser PJ, Pajevic S, Pierpaoli C, Aldroubi A: Fiber tract following in the human brain using DT-MRI data. IEICE Trans Inf & Syst E85-D:15–21, 2002.Google Scholar
  8. 8.
    Hagmann P, Jonasson L, Deffieux T, Meuli R, Thiran J, Wedeen VJ: Fibertract segmentation in position orientation space from high angular resolution diffusion MRI. NeuroImage 32:665–675, 2006.PubMedCrossRefGoogle Scholar
  9. 9.
    Friman O, Farnebäck G, Westin C: A Bayesian approach for stochastic white matter tractography. IEEE Trans Med Imaging 25:965–978, 2006.CrossRefGoogle Scholar
  10. 10.
    Staempfli P, Jaermann T, Crelier GR, Kollias S, Valavanis A, Boesiger P: Resolving fiber crossing using advanced fast marching tractography based on diffusion tensor imaging. NeuroImage 30:110–120, 2006.PubMedCrossRefGoogle Scholar
  11. 11.
    Mori S, van Zijl PCM: Fiber tracking: principles and strategies—a technical review. NMR Biomed 15:468–480, 2002.PubMedCrossRefGoogle Scholar
  12. 12.
    Dellani PR, Glaser M, Wille PR, Vucurevic G, Stadie A, Bauermann T, Tropine A, Perneczky A, von Wangenheim, A, Stoeter P: White matter fiber tracking computation based on diffusion tensor imaging for clinical applications. J Digit Imaging 20:88–97, 2007.PubMedCrossRefGoogle Scholar
  13. 13.
    Pajevic S, Aldroubi A, Basser PJ: A continuous tensor field approximation of discrete DT-MRI data for extracting microstructural and architectural features of tissue. J Magn Reson 154:85–100, 2002.PubMedCrossRefGoogle Scholar
  14. 14.
    BrainLAB. iPlan BOLD MRI Mapping. Available at Visited on 18 March 2009.
  15. 15.
    Brain Innovation B.V. BrainVoyager QX. Available at Visited on 18 March 2009.
  16. 16.
    Goebel R, Esposito F, Formisano E: Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis. Human Brain Mapping 27:392–401, 2001.CrossRefGoogle Scholar
  17. 17.
    Roebroeck A, Galuske R, Formisano E, Chiry O, Bratzke H, Ronen I, Kim DS, Goebel R: High-resolution diffusion tensor imaging and tractography of the human optic chiasm at 9.4 T. NeuroImage 39:157–186, 2008.PubMedCrossRefGoogle Scholar
  18. 18.
    Jeong W, Fletcher P, Tao R, Whitaker R: Interactive Visualization of Volumetric White Matter Connectivity in DT-MRI Using a Parallel-Hardware Hamilton-Jacobi Solver. IEEE Trans Vis Comp Graph 13:1480–1487, 2007.PubMedCrossRefGoogle Scholar
  19. 19.
    Petrovic V, Fallon J, Kuester F: Visualizing Whole-Brain DTI Tractography with GPU-based Tuboids and LoD Management. IEEE Trans Vis Comp Graph 13:1488–1495, 2007.PubMedCrossRefGoogle Scholar
  20. 20.
    McGraw T, Nadar M: Stochastic DT-MRI Connectivity Mapping on the GPU. IEEE Trans Vis Comp Graph. 13:1504–1511, 2007.PubMedCrossRefGoogle Scholar
  21. 21.
    Mittmann A, Comunello E, von Wangenheim A: Diffusion tensor fiber tracking on graphics processing units. Comput Med Imaging Graph 32:521–530, 2008.PubMedCrossRefGoogle Scholar
  22. 22.
    Mittmann A, Dantas MAR, von Wangenheim A: Design and Implementation of Brain Fiber Tracking for GPUs and PC Clusters. Proc. 21st International Symposium on Computer Architecture and High Performance Computing SBAC-PAD, São Paulo, 101–108, 2009.Google Scholar
  23. 23.
    Köhn A, Klein J, Weiler F, Peitgen H-O: A GPU-based fiber tracking framework using geometry shaders. Proc. of SPIE Medical Imaging, Orlando, 72611J–72611J10, 2009.Google Scholar
  24. 24.
    Kwatra A, Prasanna V, Singh M: Accelerating DTI tractography using FPGAs. Proc. 20th International Parallel and Distributed Processing Symposium IPDPS, 1–8, 2006.Google Scholar
  25. 25.
    Owens JD, Luebke D, Govindaraju N, Harris M, Krüger J, Lefohn AE, Purcell TJ: A Survey of General-Purpose Computation on Graphics Hardware. Eurographics 2005, State of the Art Reports, 21–51, 2005.Google Scholar
  26. 26.
    PC Perspective. NVIDIA Tesla High Performance Computing—GPUs Take a New Life. Available at Accessed 18 March 2009.
  27. 27.
    Buck I: GPU computing with NVIDIA CUDA. ACM SIGGRAPH 2007 courses.Google Scholar
  28. 28.
    NVIDIA. NVIDIA website. Available at Accessed 14 November 2008.
  29. 29.
    Mark WR, Glanville RS, Akeley K, Kilgard MJ: Cg: a system for programming graphics hardware in a C-like language. Proceedings of the ACM SIGGRAPH conference, San Diego, 896–907, 2003.Google Scholar
  30. 30.
    Bammer R, Auer M: Correction of eddy-current induced image warping in diffusion-weighted single-shot EPI using constrained non-rigid mutual information image registration. Proc. 9th ISMRM, Glasgow, 2001.Google Scholar
  31. 31.
    Skare S, Anderson JLR: Simultaneous correction of eddy currents and motion in DTI using the residual error of the diffusion tensor: comparisons with mutual information. Proc. 10th ISMRM, Hawaii, 2002.Google Scholar
  32. 32.
    Newegg. Available at Accessed 18 March 2008.
  33. 33.
    NVIDIA. NVIDIA GeForce family. Available at Accessed November 14, 2008.

Copyright information

© Society for Imaging Informatics in Medicine 2010

Authors and Affiliations

  • Adiel Mittmann
    • 1
  • Tiago H. C. Nobrega
    • 1
  • Eros Comunello
    • 1
  • Juliano P. O. Pinto
    • 2
  • Paulo R. Dellani
    • 3
  • Peter Stoeter
    • 4
  • Aldo von Wangenheim
    • 1
  1. 1.Universidade Federal de Santa CatarinaDepartamento de Informática e EstatísticaFlorianópolisBrazil
  2. 2.University HospitalFederal University of Santa CatarinaFlorianópolisBrazil
  3. 3.Department of NeurologyJohannes Gutenberg University of MainzMainzGermany
  4. 4.Institute for NeuroradiologyJohannes Gutenberg University of MainzMainzGermany

Personalised recommendations