Skip to main content

Uncertainty in diffusion tensor based fibre tracking

  • Chapter
Medical Technologies in Neurosurgery

Part of the book series: Acta Neurochirurgica Supplements ((NEUROCHIRURGICA,volume 98))

Summary

Background. diffusion tensor imaging and related fibre tracking techniques have the potential to identify major white matter tracts afflicted by an individual pathology or tracts at risk for a given surgical approach. However, the reliability of these techniques is known to be limited by image distortions, image noise, low spatial resolution, and the problem of identifying crossing fibres. This paper intends to bridge the gap between the requirements of neurosurgical applications and basic research on fibre tracking uncertainty.

Method.We acquired echo planar diffusion tensor data from both 1.5 T and 3.0 T scanners. For fibre tracking, an extended deflectionbased algorithm is employed with enhanced robustness to impaired fibre integrity such as caused by diffuse or infiltrating pathological processes. Moreover, we present a method to assess and visualize the uncertainty of fibre reconstructions based on variational complex Gaussian noise, which provides an alternative to the bootstrap method. We compare fibre tracking results with and without variational noise as well as with artificially decreased image resolution and signal-to-noise.

Findings. Using our fibre tracking technique, we found a high robustness to decreased image resolution and signal-to-noise. Still, the effects of image quality on the tracking result will depend on the employed fibre tracking algorithm and must be handled with care, especially when being used for neurosurgical planning or resection guidance. An advantage of the variational noise approach over the bootstrap technique is that it is applicable to any given set of diffusion tensor images.

Conclusions. We conclude that the presented approach allows for investigating the uncertainty of diffusion tensor imaging based fibre tracking and might offer a perspective to overcome the problem of size underestimation observed by existing techniques.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Anderson AW (2001) Theoretical analysis of the effects of noise on diffusion tensor imaging. Magn Reson Med 46(6): 1174–1188

    Article  PubMed  CAS  Google Scholar 

  2. Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44 (4): 625–632

    Article  PubMed  CAS  Google Scholar 

  3. Chen B, Hsu EW (dy2005) Noise removal in magnetic resonance diffusion tensor imaging. Magn Reson Med 54 (2): 393–401

    Article  PubMed  Google Scholar 

  4. Efron B (1979) Bootstrap methods: another look at the jackknife. Ann Statist 7: 1–16

    Google Scholar 

  5. Gössl C, Fahrmeir L, Pütz B, Auer LM, Auer DP (2002) Fiber tracking from DTI using linear state space models: detectability of the pyramidal tract. Neuroimage 16 (2): 378–388

    Article  PubMed  Google Scholar 

  6. Gudbjartsson H, Patz S (1995) The Rician distribution of noisy MRI data. Magn Reson Med 34: 910–914

    Article  PubMed  CAS  Google Scholar 

  7. Hagmann P, Thiran JP, Jonasson L, Vandergheynst P, Clarke S, Maeder P, Meulib R (2003) DTI mapping of human brain connectivity: statistical fibre tracking and virtual dissection.Neuroimage 19 (3): 545–554

    Article  PubMed  CAS  Google Scholar 

  8. Jones DK (2003) Determining and visualizing uncertainty in estimates of fiber orientation from diffusion tensor MRI. Magn Reson Med 49 (1): 7–12

    Article  PubMed  Google Scholar 

  9. Jones DK, Pierpaoli C (2005a) Confidence mapping in diffusion tensor magnetic resonance imaging tractography using a bootstrap approach. Magn Reson Med 53(5): 1143–1149

    Article  PubMed  Google Scholar 

  10. Jones DK, Travis AR, Greg G, Pierpaoli C, Basser PJ (2005b) PASTA: pointwise assessment of streamline tractography attributes. Magn Reson Med 53 (6): 1462–1467

    Article  PubMed  Google Scholar 

  11. Kinoshita M, Yamada K, Hashimoto N, Kato A, Izumoto S, Baba T, Maruno M, Nishimura T, Yoshiminie T (2005) Fibertracking does not accurately estimate size of fiber bundle in pathological condition: initial neurosurgical experience using neuronavigation and subcortical white matter stimulation. Neuroimage 25 (2): 424–429

    Article  PubMed  Google Scholar 

  12. Lazar M, Alexander AL (2003) An error analysis of white matter tractography methods: synthetic diffusion field simulations. Neuroimage 20 (2): 1140–1153

    Article  PubMed  Google Scholar 

  13. LeBihan D (2003) Looking into the functional architecture of the brain with diffusion MRI. Nat Rev Neurosci 4 (6): 469–480

    Article  CAS  Google Scholar 

  14. Leemans A, Sijbers J, Verhoye M, Van der Linden A, Van Dyck D (2005) Mathematical framework for simulating diffusion tensor MR neural fiber bundles. Magn Reson Med 53 (4): 944–953

    Article  PubMed  CAS  Google Scholar 

  15. Lin CP, Tseng WYI, Cheng HC, Chen JH (2001) Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts. Neuroimage 14 (5): 1035–1047

    Article  PubMed  CAS  Google Scholar 

  16. Lori NF, Akbudak E, Shimony JS, Cull TS, Snyder AZ, Guillory RK, Conturo TE (2002) diffusion tensor fiber tracking of human brain connectivity: acquisition methods, reliability analysis, and biological results. NMR Biomed 15 (7-8): 494–515

    Article  PubMed  CAS  Google Scholar 

  17. McGraw T, Vemuri BC, Chen Y, Rao M, Mareci T (2004) DTMRI denoising and neuronal fiber tracking. Med Image Anal 8 (2): 95–111

    Article  PubMed  CAS  Google Scholar 

  18. Mori S, Crain BJ, Chacko VP, van Zijl PCM (1999) Threedimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45 (2): 265–269

    Article  PubMed  CAS  Google Scholar 

  19. Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, Fahlbusch R (2005a) Intraoperative diffusion tensor MRimaging: shifting of white matter tracts during neurosurgical procedures-initial experience. Radiology 234 (1): 218–225

    Article  PubMed  Google Scholar 

  20. Nimsky C, Ganslandt O, Hastreiter P, Wang R, Benner T, Sorensen AG, Fahlbusch R (2005b) Preoperative and intraoperative diffusion tensor imaging-based fiber tracking in glioma surgery. Neurosurgery 56 (1): 130–137

    PubMed  Google Scholar 

  21. Koch MA, Glauche V, Finsterbusch J, Nolte UG, Frahm J, Weiller C, Buchel C (2002) Distortion-free diffusion tensor imaging of cranial nerves and of inferior temporal and orbitofrontal white matter. Neuroimage 17 (1): 497–506

    Article  PubMed  CAS  Google Scholar 

  22. Pajevic S, Basser PJ (1999) Non-parametric statistical analysis of diffusion tensor MRI data using the bootstrap method. 7th Annual Meeting ISMRM, p 1790

    Google Scholar 

  23. Schlüter M, Konrad-Verse O, Hahn HK, Stieltjes B, Rexilius J, Peitgen HO (2005a) White matter lesion phantom for diffusion tensor data and its application to the assessment of fiber tracking. Medical imaging 2005, physiology, function, and structure from medical images. Proc. SPIE Vol 5746: 835–844

    Google Scholar 

  24. Schlüter M, Stieltjes B, Hahn HK, Rexilius J, Konrad-Verse O, Peitgen HO (2005b) Detection of tumor infiltration in white matter fiber bundles using diffusion tensor imaging. Int J Medical Robotics and Computer Assisted Surg 1 (3): 80–86

    Article  Google Scholar 

  25. Schwartz RB, Hsu L, Wong TZ, Kacher DF, Zamani AA, Black PM, Alexander E 3rd, Stieg PE, Moriarty TM, Martin CA, Kikinis R, Jolesz FA (1999) Intraoperative MR imaging guidance for intracranial neurosurgery: experience with the first 200_cases. Radiology 211 (2): 477–488

    PubMed  CAS  Google Scholar 

  26. Sijbers J, den Dekker AJ, Van Audekerke J, Verhoye M, Van Dyck D (1998) Estimation of the noise in magnitudeMRimages Magn Reson Imaging 16 (1): 87–90

    Article  PubMed  CAS  Google Scholar 

  27. Tournier JD, Calamante F, King MD, Gadian DG, Connelly A (2002) Limitations and requirements of diffusion tensor fiber tracking: an assessment using simulations. Magn Reson Med 47 (4): 701–708

    Article  PubMed  Google Scholar 

  28. Weinstein DM, Kindlmann GL, Lundberg EC (1999). Tensorlines: advection-diffusion based propagation through diffusion tensor fields. IEEEVisualization Proc., San Fransisco, p 249–253

    Google Scholar 

  29. Westin CF, Maier SE, Mamata H, Nabavi A, Jolesz FA, Kikinis R (2002) Processing and visualization for diffusion tensor MRI. Med Image Anal 6 (2): 93–108

    Article  PubMed  Google Scholar 

  30. Yamada K, Kizu O, Mori S, Ito H, Nakamura H, Yuen S, Kubota T, Tanaka O, Akada W, Sasajima H, Mineura K, Nishimura T (2003) Brain fiber tracking with clinically feasible diffusion-tensor MR imaging: initial experience. Radiology 227 (1): 295–301

    Article  PubMed  Google Scholar 

  31. MeVisLab development environment for medical image processing and visualization Version 1.2 (2005); http://www.mevislab.de

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2006 Springer-Verlag/Wien

About this chapter

Cite this chapter

Hahn, H.K., Klein, J., Nimsky, C., Rexilius, J., Peitgen, HO. (2006). Uncertainty in diffusion tensor based fibre tracking. In: Nimsky, C., Fahlbusch, R. (eds) Medical Technologies in Neurosurgery. Acta Neurochirurgica Supplements, vol 98. Springer, Vienna. https://doi.org/10.1007/978-3-211-33303-7_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-211-33303-7_6

  • Publisher Name: Springer, Vienna

  • Print ISBN: 978-3-211-33302-0

  • Online ISBN: 978-3-211-33303-7

  • eBook Packages: MedicineMedicine (R0)

Publish with us

Policies and ethics