Repeated Tractography of a Single Subject: How High Is the Variance?

  • Xuan GuEmail author
  • Anders Eklund
  • Hans Knutsson
Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


We have investigated the test-retest reliability of diffusion tractography, using 32 diffusion datasets from a single healthy subject. Preprocessing was carried out using functions in FSL (FMRIB Software Library), and tractography was carried out using FSL and Dipy. The tractography was performed in diffusion space, using two seed masks (corticospinal and cingulum gyrus tracts) created from the JHU White-Matter Tractography atlas. The tractography results were then warped into MNI standard space by a linear transformation. The reproducibility of tract metrics was examined using the standard deviation, the coefficient of variation (CV) and the Dice similarity coefficient (DSC), which all indicated a high reproducibility. Our results show that the multi-fiber model in FSL is able to reveal more connections between brain areas, compared to the single fiber model, and that distortion correction increases the reproducibility.



We thank Russell Poldrack and his colleagues for sharing the data from the MyConnectome project. We also thank Cyril Pernet and his colleagues for sharing neuroimaging data from brain tumor patients. The Nvidia Corporation is acknowledged for the donation of the Tesla K40 graphics card. This research was supported by the Information Technology for European Advancement (ITEA) 3 Project BENEFIT (better effectiveness and efficiency by measuring and modelling of interventional therapy) and the Swedish Research Council (grant 2015-05356, “Learning of sets of diffusion MRI sequences for optimal imaging of micro structures”). Anders Eklund was also supported by Swedish Research Council Grant 2013-5229 (“Statistical Analysis of fMRI Data”).


  1. 1.
    Aganj, I., Lenglet, C., Sapiro, G., Yacoub, E., Ugurbil, K., Harel, N.: Reconstruction of the orientation distribution function in single-and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64(2), 554–566 (2010)Google Scholar
  2. 2.
    Andersson, J.L., Sotiropoulos, S.N.: An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. NeuroImage 125, 1063–1078 (2016)CrossRefGoogle Scholar
  3. 3.
    Andersson, J.L., Skare, S., Ashburner, J.: How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20(2), 870–888 (2003)CrossRefGoogle Scholar
  4. 4.
    Basser, P.J., Pajevic, S., Pierpaoli, C., Duda, J., Aldroubi, A.: In vivo fiber tractography using DT-MRI data. Magn. Reson. Med. 44(4), 625–632 (2000)CrossRefGoogle Scholar
  5. 5.
    Bauer, M.H., Kuhnt, D., Barbieri, S., Klein, J., Becker, A., Freisleben, B., Hahn, H.K., Nimsky, C.: Reconstruction of white matter tracts via repeated deterministic streamline tracking–initial experience. PloS One 8(5), e63082 (2013)CrossRefGoogle Scholar
  6. 6.
    Behrens, T., Berg, H.J., Jbabdi, S., Rushworth, M., Woolrich, M.: Probabilistic diffusion tractography with multiple fibre orientations: what can we gain? Neuroimage 34(1), 144–155 (2007)CrossRefGoogle Scholar
  7. 7.
    Besseling, R.M., Jansen, J.F., Overvliet, G.M., Vaessen, M.J., Braakman, H.M., Hofman, P.A., Aldenkamp, A.P., Backes, W.H.: Tract specific reproducibility of tractography based morphology and diffusion metrics. PloS One 7(4), e34125 (2012)CrossRefGoogle Scholar
  8. 8.
    Bizzi, A.: Diffusion imaging with MR tractography for brain tumor surgery. In: Clinical Functional MRI, pp. 179–228. Springer, Berlin (2015)Google Scholar
  9. 9.
    Brown, C.E.: Coefficient of variation. In: Applied Multivariate Statistics in Geohydrology and Related Sciences, pp. 155–157. Springer, Berlin (1998)Google Scholar
  10. 10.
    Caan, M.W.: DTI analysis methods: fibre tracking and connectivity. In: Diffusion Tensor Imaging, pp. 205–228. Springer, Berlin (2016)Google Scholar
  11. 11.
    Castellano, A., Bello, L., Michelozzi, C., Gallucci, M., Fava, E., Iadanza, A., Riva, M., Casaceli, G., Falini, A.: Role of diffusion tensor magnetic resonance tractography in predicting the extent of resection in glioma surgery. Neuro-Oncology 14(2), 192–202 (2012)CrossRefGoogle Scholar
  12. 12.
    Côté, M.-A., Girard, G., Boré, A., Garyfallidis, E., Houde, J.-C., Descoteaux, M.: Tractometer: towards validation of tractography pipelines. Med. Image Anal. 17(7), 844–857 (2013)CrossRefGoogle Scholar
  13. 13.
    Cutajar, M., Clayden, J.D., Clark, C.A., Gordon, I.: Test–retest reliability and repeatability of renal diffusion tensor MRI in healthy subjects. Eur. J. Radiol. 80(3), e263–e268 (2011)CrossRefGoogle Scholar
  14. 14.
    Danielian, L.E., Iwata, N.K., Thomasson, D.M., Floeter, M.K.: Reliability of fiber tracking measurements in diffusion tensor imaging for longitudinal study. Neuroimage 49(2), 1572–1580 (2010)CrossRefGoogle Scholar
  15. 15.
    Dice, L.R.: Measures of the amount of ecologic association between species. Ecology 26(3), 297–302 (1945)CrossRefGoogle Scholar
  16. 16.
    Fillard, P., Descoteaux, M., Goh, A., Gouttard, S., Jeurissen, B., Malcolm, J., Ramirez-Manzanares, A., Reisert, M., Sakaie, K., Tensaouti, F., et al.: Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56(1), 220–234 (2011)CrossRefGoogle Scholar
  17. 17.
    Garyfallidis, E., Brett, M., Amirbekian, B., Rokem, A., Van Der Walt, S., Descoteaux, M., Nimmo-Smith, I.: Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8, 8 (2014)CrossRefGoogle Scholar
  18. 18.
    Hagmann, P., Gigandet, X., Meuli, R., Kötter, R., Sporns, O., Wedeen, V.: Quantitative validation of MR tractography using the cocomac database. In: Proceedings of 16th Annual Meeting of the ISMRM, EPFL-CONF-135048, p. 427 (2008)Google Scholar
  19. 19.
    Heiervang, E., Behrens, T., Mackay, C., Robson, M., Johansen-Berg, H.: Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage 33(3), 867–877 (2006)CrossRefGoogle Scholar
  20. 20.
    Hernández, M., Guerrero, G.D., Cecilia, J.M., García, J.M., Inuggi, A., Jbabdi, S., Behrens, T.E., Sotiropoulos, S.N.: Accelerating fibre orientation estimation from diffusion weighted magnetic resonance imaging using GPUs. PloS One 8(4), e61892 (2013)CrossRefGoogle Scholar
  21. 21.
    Hua, K., Zhang, J., Wakana, S., Jiang, H., Li, X., Reich, D.S., Calabresi, P.A., Pekar, J.J., van Zijl, P.C., Mori, S.: Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39(1), 336–347 (2008)CrossRefGoogle Scholar
  22. 22.
    Iliescu, B., Negru, D., Poeata, I.: MR tractography for preoperative planning in patients with cerebral tumors in eloquent areas. Rom. Neurosurg. 17(4), 413–420 (2010)Google Scholar
  23. 23.
    Jenkinson, M., Smith, S.: A global optimisation method for robust affine registration of brain images. Med. Image Anal. 5(2), 143–156 (2001)CrossRefGoogle Scholar
  24. 24.
    Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. Neuroimage 62(2), 782–790 (2012)CrossRefGoogle Scholar
  25. 25.
    Jeurissen, B., Leemans, A., Tournier, J.-D., Jones, D.K., Sijbers, J.: Investigating the prevalence of complex fiber configurations in white matter tissue with diffusion magnetic resonance imaging. Hum. Brain Mapp. 34(11), 2747–2766 (2013)CrossRefGoogle Scholar
  26. 26.
    Laumann, T.O., Gordon, E.M., Adeyemo, B., Snyder, A.Z., Joo, S.J., Chen, M.-Y., Gilmore, A.W., McDermott, K.B., Nelson, S.M., Dosenbach, N.U., Schlaggar, B.L., Mumford, J.A., Poldrack, R.A., Petersen, S.E.: Functional system and areal organization of a highly sampled individual human brain. Neuron 87, 657–670 (2015)CrossRefGoogle Scholar
  27. 27.
    Mastronardi, L., Bozzao, A., D’Andrea, G., Romano, A., Caroli, M., Cipriani, V., Ferrante, M., Ferrante, L.: Use of preoperative and intraoperative magnetic resonance tractography in intracranial tumor surgery. Clin. Neurosurg. 55, 160–164 (2008)Google Scholar
  28. 28.
    Mori, S., Wakana, S., Van Zijl, P.C., Nagae-Poetscher, L.: MRI Atlas of Human White Matter. Elsevier, Amsterdam (2005)Google Scholar
  29. 29.
    Neher, P.F., Descoteaux, M., Houde, J.-C., Stieltjes, B., Maier-Hein, K.H.: Strengths and weaknesses of state of the art fiber tractography pipelines—a comprehensive in-vivo and phantom evaluation study using tractometer. Med. Image Anal. 26(1), 287–305 (2015)CrossRefGoogle Scholar
  30. 30.
    Nusbaum, A.O., Tang, C.Y., Buchsbaum, M.S., Wei, T.C., Atlas, S.W.: Regional and global changes in cerebral diffusion with normal aging. Am. J. Neuroradiol. 22(1), 136–142 (2001)Google Scholar
  31. 31.
    Pernet, C.R., Gorgolewski, K.J., Job, D., Rodriguez, D., Whittle, I., Wardlaw, J.: A structural and functional magnetic resonance imaging dataset of brain tumour patients. Sci. Data 3 (2016). doi:10.1038/sdata.2016.3Google Scholar
  32. 32.
    Pierpaoli, C., Walker, L., Irfanoglu, M., Barnett, A., Basser, P., Chang, L., Koay, C., Pajevic, S., Rohde, G., Sarlls, J., et al.: Tortoise: an integrated software package for processing of diffusion MRI data. In: ISMRM 18th Annual Meeting, Stockholm, vol. 18, p. 1597 (2010)Google Scholar
  33. 33.
    Pujol, S., Wells, W., Pierpaoli, C., Brun, C., Gee, J., Cheng, G., Vemuri, B., Commowick, O., Prima, S., Stamm, A., et al.: The DTI challenge: toward standardized evaluation of diffusion tensor imaging tractography for neurosurgery. J. Neuroimaging 25(6), 875–882 (2015)CrossRefGoogle Scholar
  34. 34.
    Tagliafico, A., Calabrese, M., Puntoni, M., Pace, D., Baio, G., Neumaier, C.E., Martinoli, C.: Brachial plexus MR imaging: accuracy and reproducibility of DTI-derived measurements and fibre tractography at 3.0-t. Eur. Radiol. 21(8), 1764–1771 (2011)Google Scholar
  35. 35.
    Tensaouti, F., Delion, M., Lotterie, J.A., Clarisse, P., Berry, I.: Reproducibility and reliability of the DTI fiber tracking algorithm integrated in the Sisyphe software. In: 2008 First Workshops on Image Processing Theory, Tools and Applications (2008)Google Scholar
  36. 36.
    Tensaouti, F., Lahlou, I., Clarisse, P., Lotterie, J.A., Berry, I.: Quantitative and reproducibility study of four tractography algorithms used in clinical routine. J. Magn. Reson. Imaging 34(1), 165–172 (2011)CrossRefGoogle Scholar
  37. 37.
    Tuch, D.S., Reese, T.G., Wiegell, M.R., Wedeen, V.J.: Diffusion MRI of complex neural architecture. Neuron 40(5), 885–895 (2003)CrossRefGoogle Scholar
  38. 38.
    Vaessen, M., Hofman, P., Tijssen, H., Aldenkamp, A., Jansen, J., Backes, W.H.: The effect and reproducibility of different clinical DTI gradient sets on small world brain connectivity measures. Neuroimage 51(3), 1106–1116 (2010)CrossRefGoogle Scholar
  39. 39.
    Vollmar, C., O’Muircheartaigh, J., Barker, G.J., Symms, M.R., Thompson, P., Kumari, V., Duncan, J.S., Richardson, M.P., Koepp, M.J.: Identical, but not the same: intra-site and inter-site reproducibility of fractional anisotropy measures on two 3.0 t scanners. Neuroimage 51(4), 1384–1394 (2010)Google Scholar
  40. 40.
    Yeh, F.-C., Verstynen, T.D., Wang, Y., Fernández-Miranda, J.C., Tseng, W.-Y.I.: Deterministic diffusion fiber tracking improved by quantitative anisotropy. PloS One 8(11), e80713 (2013)CrossRefGoogle Scholar
  41. 41.
    Yoon, B., Shim, Y.-S., Lee, K.-S., Shon, Y.-M., Yang, D.-W.: Region-specific changes of cerebral white matter during normal aging: a diffusion-tensor analysis. Arch. Gerontol. Geriatr. 47(1), 129–138 (2008)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Division of Medical Informatics, Department of Biomedical EngineeringLinköping UniversityLinköpingSweden
  2. 2.Center for Medical Image Science and VisualizationLinköping UniversityLinköpingSweden
  3. 3.Division of Statistics and Machine Learning, Department of Computer and Information ScienceLinköping UniversityLinköpingSweden

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