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Spatial correspondence of spinal cord white matter tracts using diffusion tensor imaging, fibre tractography, and atlas-based segmentation

Abstract

Purpose

Neuroimaging provides great utility in complex spinal surgeries, particularly when anatomical geometry is distorted by pathology (tumour, degeneration, etc.). Spinal cord MRI diffusion tractography can be used to generate streamlines; however, it is unclear how well they correspond with white matter tract locations along the cord microstructure. The goal of this work was to evaluate the spatial correspondence of DTI tractography with anatomical MRI in healthy anatomy (where anatomical locations can be well defined in T1-weighted images).

Methods

Ten healthy volunteers were scanned on a 3T system. T1-weighted (1 × 1 × 1 mm) and diffusion-weighted images (EPI readout, 2 × 2 × 2 mm, 30 gradient directions) were acquired and subsequently registered (Spinal Cord Toolbox (SCT)). Atlas-based (SCT) anatomic label maps of the left and right lateral corticospinal tracts were identified for each vertebral region (C2–C6) from T1 images. Tractography streamlines were generated with a customized approach, enabling seeding of specific spinal tract regions corresponding to individual vertebral levels. Spatial correspondence of generated fibre streamlines with anatomic tract segmentations was compared in unseeded regions of interest (ROIs).

Results

Spatial correspondence of the lateral corticospinal tract streamlines was good over a single vertebral ROI (Dice’s similarity coefficient (DSC) = 0.75 ± 0.08, Hausdorff distance = 1.08 ± 0.17 mm). Over larger ROI, fair agreement between tractography and anatomical labels was achieved (two levels: DSC = 0.67 ± 0.13, three levels: DSC = 0.52 ± 0.19).

Conclusion

DTI tractography produced good spatial correspondence with anatomic white matter tracts, superior to the agreement between multiple manual tract segmentations (DSC ~ 0.5). This supports further development of spinal cord tractography for computer-assisted neurosurgery.

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Data availability

Imaging data was collected without obtaining consent for public disclosure. Therefore, it will not be made public. Specific requests for the data will be considered provided that ethics approval can be obtained for the new purpose and that a suitable transfer agreement can be established.

References

  1. Beaulieu C (2002) The basis of anisotropic water diffusion in the nervous system - a technical review. NMR Biomed 15:435–455

    Article  Google Scholar 

  2. Jellison BJ, Field AS, Medow J, Lazar M, Salamat MS, Alexander AL (2004) Diffusion tensor imaging of cerebral white matter: a pictorial review of physics, fiber tract anatomy, and tumor imaging patterns. Am J Neuroradiol 25:356–369. https://doi.org/10.1038/nrn2776

    CAS  Article  PubMed  Google Scholar 

  3. Setzer M, Murtagh RD, Murtagh FR, Eleraky M, Jain S, Marquardt G, Seifert V, Vrionis FD (2010) Diffusion tensor imaging tractography in patients with intramedullary tumors: comparison with intraoperative findings and value for prediction of tumor resectability. J Neurosurg Spine 13:371–380. https://doi.org/10.3171/2010.3.SPINE09399

    Article  PubMed  Google Scholar 

  4. Chen Z, Tie Y, Olubiyi O, Zhang F, Mehrtash A, Rigolo L, Kahali P, Norton I, Pasternak O, Rathi Y, Golby AJ, O’Donnell LJ (2016) Corticospinal tract modeling for neurosurgical planning by tracking through regions of peritumoral edema and crossing fibers using two-tensor unscented Kalman filter tractography. Int J Comput Assist Radiol Surg 11:1475–1486. https://doi.org/10.1007/s11548-015-1344-5

    Article  PubMed  PubMed Central  Google Scholar 

  5. Hua K, Zhang J, Wakana S, Jiang H, Li X, Reich DS, Calabresi PA, Pekar JJ, van Zijl PCM, Mori S (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39:336–347. https://doi.org/10.1016/j.neuroimage.2007.07.053

    Article  PubMed  Google Scholar 

  6. Martin AR, Aleksanderek I, Cohen-Adad J, Tarmohamed Z, Tetreault L, Smith N, Cadotte DW, Crawley A, Ginsberg H, Mikulis D, Fehlings MG (2015) Translating state-of-the-art spinal cord MRI techniques to clinical use: a systematic review of clinical studies utilizing DTI, MT, MWF, MRS, and fMRI. NeuroImage Clin 10:192–238. https://doi.org/10.1016/j.nicl.2015.11.019

    Article  PubMed  PubMed Central  Google Scholar 

  7. Wheeler-Kingshott CA, Stroman PW, Schwab JM, Bacon M, Bosma R, Brooks J, Cadotte DW, Carlstedt T, Ciccarelli O, Cohen-Adad J, Curt A, Evangelou N, Fehlings MG, Filippi M, Kelley BJ, Kollias S, Mackay A, Porro CA, Smith S, Strittmatter SM, Summers P, Thompson AJ, Tracey I (2014) The current state-of-the-art of spinal cord imaging: applications. Neuroimage 84:1082–1093. https://doi.org/10.1016/j.neuroimage.2013.07.014

    CAS  Article  PubMed  Google Scholar 

  8. Egger K, Hohenhaus M, Van Velthoven V, Heil S, Urbach H (2016) Spinal diffusion tensor tractography for differentiation of intramedullary tumor-suspected lesions. Eur J Radiol 85:2275–2280. https://doi.org/10.1016/j.ejrad.2016.10.018

    CAS  Article  PubMed  Google Scholar 

  9. Fujiyoshi K, Konomi T, Yamada M, Hikishima K, Tsuji O, Komaki Y, Momoshima S, Toyama Y, Nakamura M, Okano H (2013) Diffusion tensor imaging and tractography of the spinal cord: From experimental studies to clinical application. Exp Neurol 242:74–82. https://doi.org/10.1016/j.expneurol.2012.07.015

    Article  PubMed  Google Scholar 

  10. Kelley BJ, Harel NY, Kim C-Y, Papademetris X, Coman D, Wang X, Hasan O, Kaufman A, Globinsky R, Staib LH, Cafferty WBJ, Hyder F, Strittmatter SM (2014) Diffusion tensor imaging as a predictor of locomotor function after experimental spinal cord injury and recovery. J Neurotrauma 31:1362–1373. https://doi.org/10.1089/neu.2013.3238

    Article  PubMed  PubMed Central  Google Scholar 

  11. Stroman PW, Wheeler-Kingshott C, Bacon M, Schwab JM, Bosma R, Brooks J, Cadotte D, Carlstedt T, Ciccarelli O, Cohen-Adad J, Curt A, Evangelou N, Fehlings MG, Filippi M, Kelley BJ, Kollias S, Mackay A, Porro CA, Smith S, Strittmatter SM, Summers P, Tracey I (2014) The current state-of-the-art of spinal cord imaging: methods. Neuroimage 84:1070–1081. https://doi.org/10.1016/j.neuroimage.2013.04.124

    CAS  Article  PubMed  Google Scholar 

  12. Tykocki T, English P, Minks D, Krishnakumar A, Wynne-Jones G (2018) Predictive value of flexion and extension diffusion tensor imaging in the early stage of cervical myelopathy. Neuroradiology 60:1181–1191. https://doi.org/10.1007/s00234-018-2097-y

    Article  PubMed  Google Scholar 

  13. Ellingson BM, Cohen-Adad J (2014) Diffusion-weighted imaging of the spinal cord. In: Quantitative MRI of the spinal cord. Academic Press, pp 123–145

  14. Cui J-L, Li X, Chan T-Y, Mak K-C, Luk KD-K, Hu Y (2015) Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography. Eur Spine J 24:41–47. https://doi.org/10.1007/s00586-014-3522-5

    Article  PubMed  Google Scholar 

  15. Vargas MI, Delavelle J, Jlassi H, Rilliet B, Viallon M, Becker CD, Lövblad KO (2008) Clinical applications of diffusion tensor tractography of the spinal cord. Neuroradiology 50:25–29. https://doi.org/10.1007/s00234-007-0309-y

    Article  PubMed  Google Scholar 

  16. Hendrix P, Griessenauer CJ, Cohen-Adad J, Rajasekaran S, Cauley KA, Shoja MM, Pezeshk P, Shane Tubbs R (2014) Spinal diffusion tensor imaging: a comprehensive review with emphasis on spinal cord anatomy and clinical applications. Clin Anat 95:88–95. https://doi.org/10.1002/ca.22349

    Article  Google Scholar 

  17. De Leener B, Lévy S, Dupont SM, Fonov VS, Stikov N, Louis Collins D, Callot V, Cohen-Adad J (2016) SCT: Spinal Cord Toolbox, an open-source software for processing spinal cord MRI data. Neuroimage 0–1. https://doi.org/10.1016/j.neuroimage.2016.10.009

  18. Fonov VS, Le Troter A, Taso M, De Leener B, Lévêque G, Benhamou M, Sdika M, Benali H, Pradat P-F, Collins DL, Callot V, Cohen-Adad J (2014) Framework for integrated MRI average of the spinal cord white and gray matter: The MNI-Poly-AMU template. Neuroimage 102(Pt 2):817–827. https://doi.org/10.1016/j.neuroimage.2014.08.057

    Article  PubMed  Google Scholar 

  19. Lévy S, Benhamou M, Naaman C, Rainville P, Callot V, Cohen-Adad J (2015) White matter atlas of the human spinal cord with estimation of partial volume effect. Neuroimage 119:262–271. https://doi.org/10.1016/j.neuroimage.2015.06.040

    Article  PubMed  Google Scholar 

  20. De Leener B, Fonov VS, Collins DL, Callot V, Stikov N, Cohen-Adad J (2018) PAM50: unbiased multimodal template of the brainstem and spinal cord aligned with the ICBM152 space. Neuroimage 165:170–179. https://doi.org/10.1016/j.neuroimage.2017.10.041

    Article  PubMed  Google Scholar 

  21. De Leener B, Cohen-Adad J, Kadoury S (2014) Automatic 3D segmentation of spinal cord MRI using propagated deformable models. SPIE Med Imaging 9034:90343R. https://doi.org/10.1117/12.2043183

    Article  Google Scholar 

  22. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin J-C, Pujol S, Bauer C, Jennings D, Fennessy F, Sonka M, Buatti J, Aylward S, Miller JV, Pieper S, Kikinis R (2012) 3D Slicer as an image computing platform for the Quantitative Imaging Network. Magn Reson Imaging 30:1323–1341. https://doi.org/10.1016/j.mri.2012.05.001

    Article  PubMed  PubMed Central  Google Scholar 

  23. Norton I, Essayed WI, Zhang F, Pujol S, Yarmarkovich A, Golby AJ, Kindlmann G, Wasserman D, Estepar RSJ, Rathi Y, Pieper S, Kikinis R, Johnson HJ, Westin CF, O’Donnell LJ (2017) SlicerDMRI: open source diffusion MRI software for brain cancer research. Cancer Res 77:e101–e103. https://doi.org/10.1158/0008-5472.CAN-17-0332

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Andersson JLR, Skare S, Ashburner J (2003) How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. Neuroimage 20:870–888. https://doi.org/10.1016/S1053-8119(03)00336-7

    Article  PubMed  Google Scholar 

  25. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TEJ, Johansen-Berg H, Bannister PR, De Luca M, Drobnjak I, Flitney DE, Niazy RK, Saunders J, Vickers J, Zhang Y, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219. https://doi.org/10.1016/J.NEUROIMAGE.2004.07.051

    Article  PubMed  Google Scholar 

  26. Alexander DC, Pierpaoli C, Basser PJ, Gee JC (2001) Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans Med Imaging 20:1131–1139. https://doi.org/10.1109/42.963816

    CAS  Article  PubMed  Google Scholar 

  27. Dupont SM, De Leener B, Taso M, Le Troter A, Nadeau S, Stikov N, Callot V, Cohen-Adad J (2017) Fully-integrated framework for the segmentation and registration of the spinal cord white and gray matter. Neuroimage 150:358–372. https://doi.org/10.1016/j.neuroimage.2016.09.026

    Article  PubMed  Google Scholar 

  28. Maki S, Koda M, Saito J, Takahashi S, Inada T, Kamiya K, Ota M, Iijima Y, Masuda Y, Matsumoto K, Kojima M, Takahashi K, Obata T, Masashi Yamazaki TF (2016) Tract-specific diffusion tensor imaging reveals laterality of neurological symptoms in patients with cervical compression myelopathy. World Neurosurg 96:184–190. https://doi.org/10.1016/j.wneu.2016.08.129

    Article  PubMed  Google Scholar 

  29. Lundell H, Barthelemy D, Biering-Sørensen F, Cohen-Adad J, Nielsen JB, Dyrby TB (2013) Fast diffusion tensor imaging and tractography of the whole cervical spinal cord using point spread function corrected echo planar imaging. Magn Reson Med 69:144–149. https://doi.org/10.1002/mrm.24235

    Article  PubMed  Google Scholar 

  30. Bosma R, Stroman PW (2012) Diffusion tensor imaging in the human spinal cord: development, limitations, and clinical applications. Crit Rev Biomed Eng 40:1–20. https://doi.org/10.1615/CritRevBiomedEng.v40.i1.10

    CAS  Article  PubMed  Google Scholar 

  31. Ducreux D, Fillard P, Facon D, Ozanne A, Lepeintre JF, Renoux J, Tadi M, Lasjaunias P (2007) Diffusion tensor magnetic resonance imaging and fiber tracking in spinal cord lesions: current and future indications. Neuroimaging Clin N Am 17:137–147. https://doi.org/10.1016/j.nic.2006.11.005

    Article  PubMed  Google Scholar 

  32. Dauleac C, Frindel C, Mertens P, Jacquesson T, Cotton F (2020) Overcoming challenges of the human spinal cord tractography for routine clinical use: a review. Neuroradiology 62:1079–1094

    Article  Google Scholar 

  33. Wang K, Chen Z, Zhang F, Song Q, Hou C, Tang Y, Wang J, Chen S, Bian Y, Hao Q, Shen H (2016) Evaluation of DTI parameter ratios and diffusion tensor tractography grading in the diagnosis and prognosis prediction of cervical spondylotic myelopathy

  34. Tournier JD, Calamante F, Connelly A (2012) MRtrix: diffusion tractography in crossing fiber regions. Int J Imaging Syst Technol 22:53–66. https://doi.org/10.1002/ima.22005

    Article  Google Scholar 

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Funding

This work was supported by research funding provided by FedDev Ontario, Mitacs post-doctoral fellowship support, and the Feldberg Chair for Spinal Research. Image datasets, in kind resources, and technical support were provided by Synaptive Medical Inc.

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Corresponding author

Correspondence to Michael Raymond Hardisty.

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Conflict of interest

Stewart McLachlin’s stipend during the research project was provided by a Mitacs fellowship with matching funds from Synaptive Medical. Jason Leung has no conflicts. Vignesh Sivan has no conflicts. Pierre-Olivier Quirion has no conflicts. Phoenix Wilkie has no conflicts. Julien Cohen-Adad has no conflicts. Cari Marisa Whyne received grant matching funds for the research reported in this article from Synaptive Medical. Michael Raymond Hardisty has no conflicts.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

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Informed consent was obtained from all individual participants included in the study (include appropriate statements).

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Granted.

Code availability

The code is available open source at https://bitbucket.org/OrthopaedicBiomechanicsLab/sctwrappers/src/master/.

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McLachlin, S., Leung, J., Sivan, V. et al. Spatial correspondence of spinal cord white matter tracts using diffusion tensor imaging, fibre tractography, and atlas-based segmentation. Neuroradiology 63, 373–380 (2021). https://doi.org/10.1007/s00234-021-02635-9

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  • DOI: https://doi.org/10.1007/s00234-021-02635-9

Keywords

  • Spinal cord
  • Diffusion tensor imaging
  • Tractography
  • Segmentation
  • Deformable registration