Surgical and Radiologic Anatomy

, Volume 34, Issue 8, pp 709–719 | Cite as

Corticospinal tractography with morphological, functional and diffusion tensor MRI: a comparative study of four deterministic algorithms used in clinical routine

  • Romuald Seizeur
  • Nicolas Wiest-Daessle
  • Sylvain Prima
  • Camille Maumet
  • Jean-Christophe Ferre
  • Xavier Morandi
Original Article



Diffusion tensor imaging permits study of white matter fibre bundles; however, its main limitation is lack of validation on anatomical data, especially in crossing fibre regions. Our study aimed to compare four deterministic tractography algorithms used in clinical routine. We studied the corticospinal tract, the bundle mediating voluntary movement. Our study seeks to evaluate tractography provided by algorithms through comparative analysis by expert neuroradiologists.


MRI data from 15 right-handed volunteers (30.8 years) were studied. Regions of interest (ROIs) were segmented on morphological and functional MRI. Diffusion weighted images (15 directions) were performed, then for each voxel the tensor was estimated. Tractography of the corticospinal tract was performed using four fibre-tracking algorithms. Three numerical integration methods Euler, Runge–Kutta second (RK2) and fourth order (RK4), and a tensor deflection method (TEND). Quantitative measurement was performed. Qualitative evaluation was carried out by two expert neuroradiologists using Kappa test concordance.


For the quantitative aspect, only RK2 and TEND presented no significant difference concerning the number of fibres (p = 0.58). There was no difference between right and left side for each algorithm. Regarding the qualitative aspects, there was a lack of fibres from the ventrolateral part of the functional ROIs. Comparison by expert neuroradiologists revealed low rather than high concordance. The algorithm ranked first was RK2 according to expert preferences.


Different algorithms used in clinical routine failed to show realistic anatomical bundles. The most mathematically robust algorithm was not selected, nor was the algorithm defining more fibres. Validation of anatomical data provided by tractography remains a challenge.


Corticospinal tract Deterministic tractography Diffusion tensor imaging Anatomy MRI 



The authors would like to warmly thank Pierre Forlodou and Douraied Ben Salem, neuroradiologists in Brest, for their assistance in the realisation of this study.

Conflict of interest

The authors declare no conflict of interest in the realisation of this study


  1. 1.
    Annett M (1970) A classification of hand preference by association analysis. Br J Psychol 61:303–321PubMedCrossRefGoogle Scholar
  2. 2.
    Bammer R, Auer M, Keeling SL, Augustin M, Stables LA, Prokesch RW, Stollberger R, Moseley ME, Fazekas F (2002) Diffusion tensor imaging using single-shot SENSE-EPI. Magn Reson Med 48:128–136PubMedCrossRefGoogle Scholar
  3. 3.
    Basser PJ, Pajevic S, Pierpaoli C, Duda J, Aldroubi A (2000) In vivo fiber tractography using DT-MRI data. Magn Reson Med 44:625–632PubMedCrossRefGoogle Scholar
  4. 4.
    Burgel U, Madler B, Honey CR, Thron A, Gilsbach J, Coenen VA (2009) Fiber tracking with distinct software tools results in a clear diversity in anatomical fiber tract portrayal. Cen Eur Neurosurg 70:27–35CrossRefGoogle Scholar
  5. 5.
    Caulo M, Briganti C, Mattei PA, Perfetti B, Ferretti A, Romani GL, Tartaro A, Colosimo C (2007) New morphologic variants of the hand motor cortex as seen with MR imaging in a large study population. AJNR Am J Neuroradiol 28:1480–1485PubMedCrossRefGoogle Scholar
  6. 6.
    Cheng P, Magnotta VA, Wu D, Nopoulos P, Moser DJ, Paulsen J, Jorge R, Andreasen NC (2006) Evaluation of the GTRACT diffusion tensor tractography algorithm: a validation and reliability study. Neuroimage 31:1075–1085PubMedCrossRefGoogle Scholar
  7. 7.
    Cherubini A, Luccichenti G, Peran P, Hagberg GE, Barba C, Formisano R, Sabatini U (2007) Multimodal fMRI tractography in normal subjects and in clinically recovered traumatic brain injury patients. Neuroimage 34:1331–1341PubMedCrossRefGoogle Scholar
  8. 8.
    Chung HW, Chou MC, Chen CY (2011) Principles and limitations of computational algorithms in clinical diffusion tensor MR tractography. AJNR Am J Neuroradiol 32:3–13PubMedCrossRefGoogle Scholar
  9. 9.
    Ciccarelli O, Catani M, Johansen-Berg H, Clark C, Thompson A (2008) Diffusion-based tractography in neurological disorders: concepts, applications, and future developments. Lancet Neurol 7:715–727PubMedCrossRefGoogle Scholar
  10. 10.
    Ciccarelli O, Parker GJ, Toosy AT, Wheeler-Kingshott CA, Barker GJ, Boulby PA, Miller DH, Thompson AJ (2003) From diffusion tractography to quantitative white matter tract measures: a reproducibility study. Neuroimage 18:348–359PubMedCrossRefGoogle Scholar
  11. 11.
    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 USA 96:10422–10427PubMedCrossRefGoogle Scholar
  12. 12.
    Dellatolas G, De Agostini M, Jallon P, Poncet M, Rey M, Lellouch J (1988) Mesure de la préférence manuelle par autoquestionnaire dans la population française adulte. Rev Psychol appl 38:117–136Google Scholar
  13. 13.
    Descoteaux M, Deriche R, Knosche TR, Anwander A (2009) Deterministic and probabilistic tractography based on complex fibre orientation distributions. IEEE Trans Med Imaging 28:269–286PubMedCrossRefGoogle Scholar
  14. 14.
    Fernandez-Miranda JC, Rhoton AL Jr, Alvarez-Linera J, Kakizawa Y, Choi C, de Oliveira EP (2008) Three-dimensional microsurgical and tractographic anatomy of the white matter of the human brain. Neurosurgery 62:989–1026PubMedCrossRefGoogle Scholar
  15. 15.
    Fillard P, Arsigny V, Pennec X, Hayashi KM, Thompson PM, Ayache N (2007) Measuring brain variability by extrapolating sparse tensor fields measured on sulcal lines. Neuroimage 34:639–650PubMedCrossRefGoogle Scholar
  16. 16.
    Fillard P, Descoteaux M, Goh A, Gouttard S, Jeurissen B, Malcolm J, Ramirez-Manzanares A, Reisert M, Sakaie K, Tensaouti F, Yo T, Mangin JF, Poupon C (2011) Quantitative evaluation of 10 tractography algorithms on a realistic diffusion MR phantom. Neuroimage 56:220–234PubMedCrossRefGoogle Scholar
  17. 17.
    Friston KJ, Holmes AP, Worsley KJ, Poline J-P, frith CD, Frackowiak RS (1995) Statistical parametric maps in functional imaging: a general linear approach. Hum Brain Mapp 2:189–210CrossRefGoogle Scholar
  18. 18.
    Hasan KM, Walimuni IS, Abid H, Hahn KR (2011) A review of diffusion tensor magnetic resonance imaging computational methods and software tools. Comput Biol Med 41:1062–1072Google Scholar
  19. 19.
    Heiervang E, Behrens TE, Mackay CE, Robson MD, Johansen-Berg H (2006) Between session reproducibility and between subject variability of diffusion MR and tractography measures. Neuroimage 33:867–877PubMedCrossRefGoogle Scholar
  20. 20.
    Iwasaki S, Nakagawa H, Fukusumi A, Kichikawa K, Kitamura K, Otsuji H, Uchida H, Ohishi H, Yaguchi K, Sumie H (1991) Identification of pre- and postcentral gyri on CT and MR images on the basis of the medullary pattern of cerebral white matter. Radiology 179:207–213PubMedGoogle Scholar
  21. 21.
    Johansen-Berg H, Behrens TE (2006) Just pretty pictures? What diffusion tractography can add in clinical neuroscience. Curr Opin Neurol 19:379–385PubMedCrossRefGoogle Scholar
  22. 22.
    Jones DK, Horsfield MA, Simmons A (1999) Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn Reson Med 42:515–525PubMedCrossRefGoogle Scholar
  23. 23.
    Lawes IN, Barrick TR, Murugam V, Spierings N, Evans DR, Song M, Clark CA (2008) Atlas-based segmentation of white matter tracts of the human brain using diffusion tensor tractography and comparison with classical dissection. Neuroimage 39:62–79PubMedCrossRefGoogle Scholar
  24. 24.
    Lazar M, Weinstein DM, Tsuruda JS, Hasan KM, Arfanakis K, Meyerand ME, Badie B, Rowley HA, Haughton V, Field A, Alexander AL (2003) White matter tractography using diffusion tensor deflection. Hum Brain Mapp 18:306–321PubMedCrossRefGoogle Scholar
  25. 25.
    Lin CP, Tseng WY, Cheng HC, Chen JH (2001) Validation of diffusion tensor magnetic resonance axonal fiber imaging with registered manganese-enhanced optic tracts. Neuroimage 14:1035–1047PubMedCrossRefGoogle Scholar
  26. 26.
    Lotze M, Erb M, Flor H, Huelsmann E, Godde B, Grodd W (2000) fMRI evaluation of somatotopic representation in human primary motor cortex. Neuroimage 11:473–481PubMedCrossRefGoogle Scholar
  27. 27.
    Mangin JF, Poupon C, Clark C, Le BD, Bloch I (2002) Distortion correction and robust tensor estimation for MR diffusion imaging. Med Image Anal 6:191–198PubMedCrossRefGoogle Scholar
  28. 28.
    Mori S, Barker PB (1999) Diffusion magnetic resonance imaging: its principle and applications. Anat Rec 257:102–109PubMedCrossRefGoogle Scholar
  29. 29.
    Mori S, Crain BJ, Chacko VP, van Zijl PC (1999) Three-dimensional tracking of axonal projections in the brain by magnetic resonance imaging. Ann Neurol 45:265–269PubMedCrossRefGoogle Scholar
  30. 30.
    Mori S, Frederiksen K, van Zijl PC, Stieltjes B, Kraut MA, Solaiyappan M, Pomper MG (2002) Brain white matter anatomy of tumor patients evaluated with diffusion tensor imaging. Ann Neurol 51:377–380PubMedCrossRefGoogle Scholar
  31. 31.
    Oldfield RC (1971) The assessment and analysis of handedness: the Edinburgh inventory. Neuropsychologia 9:97–113PubMedCrossRefGoogle Scholar
  32. 32.
    Qazi AA, Radmanesh A, O’Donnell L, Kindlmann G, Peled S, Whalen S, Westin CF, Golby AJ (2009) Resolving crossings in the corticospinal tract by two-tensor streamline tractography: method and clinical assessment using fMRI. Neuroimage 47(Suppl 2):T98–T106PubMedCrossRefGoogle Scholar
  33. 33.
    Schimrigk SK, Bellenberg B, Schluter M, Stieltjes B, Drescher R, Rexilius J, Lukas C, Hahn HK, Przuntek H, Koster O (2007) Diffusion tensor imaging-based fractional anisotropy quantification in the corticospinal tract of patients with amyotrophic lateral sclerosis using a probabilistic mixture model. AJNR Am J Neuroradiol 28:724–730PubMedGoogle Scholar
  34. 34.
    Skare S, Hedehus M, Moseley ME, Li TQ (2000) Condition number as a measure of noise performance of diffusion tensor data acquisition schemes with MRI. J Magn Reson 147:340–352PubMedCrossRefGoogle Scholar
  35. 35.
    Stamm A, Perez P, Barillot C (2011) Diffusion directions imaging (DDI). Technical report, INRIA.
  36. 36.
    Talairach J, Tournoux P (1988) Coplanar stereotaxic atlas of the human brain, Thieme, New YorkGoogle Scholar
  37. 37.
    Tensaouti F, Lahlou I, Clarisse P, Lotterie JA, Berry I (2011) Quantitative and reproducibility study of four tractography algorithms used in clinical routine. J Magn Reson Imaging 34:165–172PubMedCrossRefGoogle Scholar
  38. 38.
    Tuch DS, Reese TG, Wiegell MR, Makris N, Belliveau JW, Wedeen VJ (2002) High angular resolution diffusion imaging reveals intravoxel white matter fiber heterogeneity. Magn Reson Med 48:577–582PubMedCrossRefGoogle Scholar
  39. 39.
    Wakana S, Caprihan A, Panzenboeck MM, Fallon JH, Perry M, Gollub RL, Hua K, Zhang J, Jiang H, Dubey P, Blitz A, van Zijl P, Mori S (2007) Reproducibility of quantitative tractography methods applied to cerebral white matter. Neuroimage 36:630–644PubMedCrossRefGoogle Scholar
  40. 40.
    Wedeen VJ, Hagmann P, Tseng WY, Reese TG, Weisskoff RM (2005) Mapping complex tissue architecture with diffusion spectrum magnetic resonance imaging. Magn Reson Med 54:1377–1386PubMedCrossRefGoogle Scholar
  41. 41.
    Wiest-Daesslé N, Prima S, Morrisey SP, Barillot C (2007) Validation of a new optimisation algorithm for registration tasks in medical imaging In: 4th IEEE International symposium on biomedical imaging: from Nano to Macro, ISBI 41–44Google Scholar
  42. 42.
    Yamada K, Sakai K, Hoogenraad FG, Holthuizen R, Akazawa K, Ito H, Oouchi H, Matsushima S, Kubota T, Sasajima H, Mineura K, Nishimura T (2007) Multitensor tractography enables better depiction of motor pathways: initial clinical experience using diffusion-weighted MR imaging with standard b-value. AJNR Am J Neuroradiol 28:1668–1673PubMedCrossRefGoogle Scholar
  43. 43.
    Yousry TA, Schmid UD, Alkadhi H, Schmidt D, Peraud A, Buettner A, Winkler P (1997) Localization of the motor hand area to a knob on the precentral gyrus. A new landmark. Brain 120(Pt 1):141–157PubMedCrossRefGoogle Scholar
  44. 44.
    Zhang Y, Zhang J, Oishi K, Faria AV, Jiang H, Li X, Akhter K, Rosa-Neto P, Pike GB, Evans A, Toga AW, Woods R, Mazziotta JC, Miller MI, van Zijl PC, Mori S (2010) Atlas-guided tract reconstruction for automated and comprehensive examination of the white matter anatomy. Neuroimage 52:1289–1301PubMedCrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Romuald Seizeur
    • 1
    • 2
  • Nicolas Wiest-Daessle
    • 1
  • Sylvain Prima
    • 1
  • Camille Maumet
    • 1
  • Jean-Christophe Ferre
    • 1
    • 3
  • Xavier Morandi
    • 1
    • 4
  1. 1.IRISA Unité VisAGeS U746INSERM/INRIA/CNRS/Université Rennes 1Rennes, CedexFrance
  2. 2.Service de Neurochirurgie, CHRU Brest, Hôpital Cavale BlancheBrestFrance
  3. 3.Service de NeuroradiologieCHU RENNES, Hôpital PontchaillouRennesFrance
  4. 4.Service de NeurochirurgieCHU RENNES, Hôpital PontchaillouRennesFrance

Personalised recommendations