Skip to main content
SpringerLink
Log in

Nonlinear deformation of tractography in ultrasound-guided low-grade gliomas resection

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

A Correction to this article was published on 01 February 2018

This article has been updated

Abstract

Purpose

In brain tumor surgeries, maximum removal of cancerous tissue without compromising normal brain functions can improve the patient’s survival rate and therapeutic benefits. To achieve this, diffusion MRI and intra-operative ultrasound (iUS) can be highly instrumental. While diffusion MRI allows the visualization of white matter tracts and helps define the resection plan to best preserve the eloquent areas, iUS can effectively track the brain shift after craniotomy that often renders the pre-surgical plan invalid, ensuring the accuracy and safety of the intervention. Unfortunately, brain shift correction using iUS and automatic registration has never been shown for brain tractography so far despite its rising significance in brain tumor resection.

Methods

We employed a correlation-ratio-based nonlinear registration algorithm to account for brain shift through MRI–iUS registration and used the recovered deformations to warp both the brain anatomy and tractography seen in pre-surgical plans. The overall technique was demonstrated retrospectively on four patients who underwent iUS-guided low-grade brain gliomas resection.

Results

Through qualitative and quantitative evaluations, the preoperative MRI and iUS scans were well realigned after nonlinear registration, and the deformed brain tumor volumes and white matter tracts showed large displacements away from the pre-surgical plans.

Conclusions

We are the first to demonstrate the technique to track nonlinear deformation of brain tractography using real clinical MRI and iUS data, and the results confirm the need for updating white matter tracts due to tissue shift during surgery.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Change history

  • 01 February 2018

    The author would like to include grant number of NSERC Discovery grant in the acknowledgement section of the original article.

References

  1. Holland EC (2001) Progenitor cells and glioma formation. Curr Opin Neurol 14:683–688

    Article  CAS  PubMed  Google Scholar 

  2. Schwartzbaum JA, Fisher JL, Aldape KD, Wrensch M (2006) Epidemiology and molecular pathology of glioma. Nat Clin Pract Neurol 2:494–503 quiz 491 p following 516

    Article  PubMed  Google Scholar 

  3. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

    Article  PubMed  PubMed Central  Google Scholar 

  4. Dolecek TA, Propp JM, Stroup NE, Kruchko C (2012) CBTRUS statistical report: primary brain and central nervous system tumors diagnosed in the United States in 2005–2009. Neuro Oncol 14(Suppl 5):v1–49

    Article  PubMed  PubMed Central  Google Scholar 

  5. Jakola AS, Myrmel KS, Kloster R, Torp SH, Lindal S, Unsgard G, Solheim O (2012) Comparison of a strategy favoring early surgical resection vs a strategy favoring watchful waiting in low-grade gliomas. J Am Med Assoc 308:1881–1888

    Article  CAS  Google Scholar 

  6. Schomas DA, Laack NNI, Rao RD, Meyer FB, Shaw EG, O’Neill BP, Giannini C, Brown PD (2009) Intracranial low-grade gliomas in adults: 30-year experience with long-term follow-up at Mayo Clinic. Neuro-Oncology 11:437–445

    Article  PubMed  PubMed Central  Google Scholar 

  7. Nimsky C, Ganslandt O, Merhof D, Sorensen AG, Fahlbusch R (2006) Intraoperative visualization of the pyramidal tract by diffusion-tensor-imaging-based fiber tracking. Neuroimage 30:1219–1229

    Article  PubMed  Google Scholar 

  8. Stadlbauer A, Ganslandt O, Buslei R, Hammen T, Gruber S, Moser E, Buchfelder M, Salomonowitz E, Nimsky C (2006) Gliomas: histopathologic evaluation of changes in directionality and magnitude of water diffusion at diffusion-tensor MR imaging. Radiology 240:803–810

    Article  PubMed  Google Scholar 

  9. Maurer CR, Hill DLG, Martin AJ, Liu HY, McCue M, Rueckert D, Lloret D, Hall WA, Maxwell RE, Hawkes DJ, Truwit CL (1998) Investigation of intraoperative brain deformation using a 1.5-t interventional MR system: preliminary results. IEEE Trans Med Imaging 17:817–825

    Article  PubMed  Google Scholar 

  10. Bucholz RD, Yeh DD, Trobaugh J, McDurmont LL, Sturm CD, Baumann C, Henderson JM, Levy A, Kessman P (1997) The correction of stereotactic inaccuracy caused by brain shift using an intraoperative ultrasound device. Lect Notes Comput Sci 1205:459–466

    Article  Google Scholar 

  11. Nimsky C, Ganslandt O, Cerny S, Hastreiter P, Greiner G, Fahlbusch R (2000) Quantification of, visualization of, and compensation for brain shift using intraoperative magnetic resonance imaging. Neurosurgery 47:1070–1079

    Article  CAS  PubMed  Google Scholar 

  12. Haberg A, Kvistad KA, Unsgard G, Haraldseth O (2004) Preoperative blood oxygen level-dependent functional magnetic resonance imaging in patients with primary brain tumors: clinical application and outcome. Neurosurgery 54:902–914 (discussion 914-905)

  13. Rasmussen IA Jr, Lindseth F, Rygh OM, Berntsen EM, Selbekk T, Xu J, Nagelhus Hernes TA, Harg E, Haberg A, Unsgaard G (2007) Functional neuronavigation combined with intra-operative 3D ultrasound: initial experiences during surgical resections close to eloquent brain areas and future directions in automatic brain shift compensation of preoperative data. Acta Neurochir 149:365–378

    Article  PubMed  Google Scholar 

  14. Stapleton SR, Kiriakopoulos E, Mikulis D, Drake JM, Hoffman HJ, Humphreys R, Hwang P, Otsubo H, Holowka S, Logan W, Rutka JT (1997) Combined utility of functional MRI, cortical mapping, and frameless stereotaxy in the resection of lesions in eloquent areas of brain in children. Pediatr Neurosurg 26:68–82

    Article  CAS  PubMed  Google Scholar 

  15. Al-Okaili RN, Krejza J, Woo JH, Wolf RL, O’Rourke DM, Judy KD, Poptani H, Melhem ER (2007) Intraaxial brain masses: MR imaging-based diagnostic strategy—initial experience. Radiology 243:539–550

    Article  PubMed  Google Scholar 

  16. Lu S, Ahn D, Johnson G, Law M, Zagzag D, Grossman RI (2004) Diffusion-tensor MR imaging of intracranial neoplasia and associated peritumoral edema: introduction of the tumor infiltration index. Radiology 232:221–228

    Article  PubMed  Google Scholar 

  17. Roldan-Valadez E, Rios C, Cortez-Conradis D, Favila R, Moreno-Jimenez S (2014) Global diffusion tensor imaging derived metrics differentiate glioblastoma multiforme vs. normal brains by using discriminant analysis: introduction of a novel whole-brain approach. Radiol Oncol 48:127–136

    Article  PubMed  PubMed Central  Google Scholar 

  18. Nimsky C, Ganslandt O, Fahlbusch R (2007) Implementation of fiber tract navigation. Neurosurgery 61:306–317 (discussion 317-308)

  19. Romano A, D’Andrea G, Minniti G, Mastronardi L, Ferrante L, Fantozzi LM, Bozzao A (2009) Pre-surgical planning and MR-tractography utility in brain tumour resection. Eur Radiol 19:2798–2808

    Article  CAS  PubMed  Google Scholar 

  20. Lu JF, Zhang H, Wu JS, Yao CJ, Zhuang DX, Qiu TM, Jia WB, Mao Y, Zhou LF (2013) “Awake” intraoperative functional MRI (ai-fMRI) for mapping the eloquent cortex: Is it possible in awake craniotomy? Neuroimage Clin 2:132–142

    Article  Google Scholar 

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

    Article  PubMed  Google Scholar 

  22. Steno A, Karlik M, Mendel P, Cik M, Steno J (2012) Navigated three-dimensional intraoperative ultrasound-guided awake resection of low-grade glioma partially infiltrating optic radiation. Acta Neurochir 154:1255–1262

    Article  PubMed  Google Scholar 

  23. Intraoperative ultrasound (ious) in neurosurgery: from standard b-mode to elastosonography. Springer, Berlin (2016)

  24. Roche A, Pennec X, Malandain G, Ayache N (2001) Rigid registration of 3-D ultrasound with MR images: a new approach combining intensity and gradient information. IEEE Trans Med Imaging 20:1038–1049

    Article  CAS  PubMed  Google Scholar 

  25. Mercier L, Fonov V, Haegelen C, Del Maestro RF, Petrecca K, Collins DL (2012) Comparing two approaches to rigid registration of three-dimensional ultrasound and magnetic resonance images for neurosurgery. Int J Comput Assist Radiol Surg 7:125–136

    Article  PubMed  Google Scholar 

  26. Arbel T, Morandi X, Comeau RM, Collins DL (2004) Automatic non-linear MRI-ultrasound registration for the correction of intra-operative brain deformations. Comput Aided Surg 9:123–136

    Article  PubMed  Google Scholar 

  27. Collins DL, Neelin P, Peters TM, Evans AC (1994) Automatic 3D intersubject registration of MR volumetric data in standardized Talairach space. J Comput Assist Tomogr 18:192–205

    Article  CAS  PubMed  Google Scholar 

  28. Kuklisova-Murgasova M, Cifor A, Napolitano R, Papageorghiou A, Quaghebeur G, Rutherford MA, Hajnal JV, Noble JA, Schnabel JA (2013) Registration of 3D fetal neurosonography and MRI. Med Image Anal 17:1137–1150

    Article  PubMed  PubMed Central  Google Scholar 

  29. Mellor M, Brady M (2005) Phase mutual information as a similarity measure for registration. Med Image Anal 9:330–343

    Article  PubMed  Google Scholar 

  30. De Nigris D, Collins DL, Arbel T (2012) Multi-modal image registration based on gradient orientations of minimal uncertainty. IEEE Trans Med Imaging 31:2343–2354

    Article  PubMed  Google Scholar 

  31. Reinertsen I, Lindseth F, Unsgaard G, Collins DL (2007) Clinical validation of vessel-based registration for correction of brain-shift. Med Image Anal 11:673–684

    Article  CAS  PubMed  Google Scholar 

  32. Reinertsen I, Descoteaux M, Siddiqi K, Collins DL (2007) Validation of vessel-based registration for correction of brain shift. Med Image Anal 11:374–388

    Article  CAS  PubMed  Google Scholar 

  33. Coupe P, Hellier P, Morandi X, Barillot C (2012) 3D rigid registration of intraoperative ultrasound and preoperative MR brain images based on hyperechogenic structures. Int J Biomed Imaging 2012:531319

    Article  PubMed  PubMed Central  Google Scholar 

  34. Ji SB, Wu ZJ, Hartov A, Roberts DW, Paulsen KD (2008) Mutual-information-based image to patient re-registration using intraoperative ultrasound in image-guided neurosurgery. Med Phys 35:4612–4624

    Article  PubMed  PubMed Central  Google Scholar 

  35. Wein W, Ladikos A, Fuerst B, Shah A, Sharma K, Navab N (2013) Global registration of ultrasound to MRI using the LC2 metric for enabling neurosurgical guidance. In: Medical image computing and computer-assisted intervention (Miccai 2013), Pt I 8149, pp 34–41

  36. Fuerst B, Wein W, Muller M, Navab N (2014) Automatic ultrasound-MRI registration for neurosurgery using the 2D and 3D LC2 Metric. Med Image Anal 18:1312–1319

    Article  PubMed  Google Scholar 

  37. Ferrante E, Paragios N (2013) Non-rigid 2D–3D medical image registration using Markov random fields. In: Medical image computing and computer-assisted intervention (Miccai 2013), Pt Iii 8151, pp 163–170

  38. Rivaz H, Chen SJS, Collins DL (2015) Automatic deformable MR-ultrasound registration for image-guided neurosurgery. IEEE T Med Imaging 34:366–380

    Article  Google Scholar 

  39. Roche A, Malandain G, Pennec X, Ayache N (1998) The correlation ratio as a new similarity measure for multimodal image registration. In: Medical image computing and computer-assisted intervention—Miccai’98 1496, pp 1115–1124

  40. Klein S, Pluim JPW, Staring M, Viergever M (2009) Adaptive stochastic gradient descent optimisation for image registration. Int J Comput Vision 81:227–239

    Article  Google Scholar 

  41. Coupe P, Yger P, Prima S, Hellier P, Kervrann C, Barillot C (2008) An optimized blockwise nonlocal means denoising filter for 3-D magnetic resonance images. IEEE Trans Med Imaging 27:425–441

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Veraart J, Novikov DS, Christiaens D, Ades-Aron B, Sijbers J, Fieremans E (2016) Denoising of diffusion MRI using random matrix theory. Neuroimage 142:384–396

    Article  Google Scholar 

  43. 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 YY, De Stefano N, Brady JM, Matthews PM (2004) Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage 23:S208–S219

    Article  PubMed  Google Scholar 

  44. Andersson JL, Sotiropoulos SN (2016) An integrated approach to correction for off-resonance effects and subject movement in diffusion MR imaging. Neuroimage 125:1063–1078

    Article  PubMed  PubMed Central  Google Scholar 

  45. Tournier J-D, Calamante F, Connelly A (2010) Improved probabilistic streamlines tractography by 2nd order integration over fibre orientation distributions. In: Proceedings of the international society for magnetic resonance in medicine 1670

  46. Fedorov A, Beichel R, Kalpathy-Cramer J, Finet J, Fillion-Robin JC, 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

    Article  PubMed  PubMed Central  Google Scholar 

  47. Gerard IJ, Kersten-Oertel M, Petrecca K, Sirhan D, Hall JA, Collins DL (2017) Brain shift in neuronavigation of brain tumors: a review. Med Image Anal 35:403–420

    Article  PubMed  Google Scholar 

  48. Campanella M, Ius T, Skrap M, Fadiga L (2014) Alterations in fiber pathways reveal brain tumor typology: a diffusion tractography study. PeerJ 2:e497

    Article  PubMed  PubMed Central  Google Scholar 

  49. Papageorgiou TS, Chourmouzi D, Drevelengas A, Kouskouras K, Siountas A (2015) Diffusion tensor imaging in brain tumors: a study on gliomas and metastases. Phys Med 31:767–773

    Article  CAS  PubMed  Google Scholar 

  50. Ferrante E, Paragios N (2017) Slice-to-volume medical image registration: a survey. Med Image Anal 39:101–123

    Article  PubMed  Google Scholar 

  51. 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 48:577–582

  52. Duffau H (2017) Mapping the connectome in awake surgery for gliomas: an update. J Neurosurg Sci 61:612–630

    PubMed  Google Scholar 

Download references

Acknowledgements

This project was partly funded by NSERC Discovery Grant and the Norwegian National Advisory Unit for Ultrasound and Image-Guided Therapy. The authors thank Dr. Maryse Fortin for her generous help in identifying the anatomical landmarks.

Author information

Authors and Affiliations

  1. PERFORM Centre, Concordia University, Rm 2.211, 7200 Sherbrooke St. W., Montreal, QC, H4B 1R6, Canada

    Yiming Xiao & Hassan Rivaz

  2. Department of Electrical and Computer Engineering, Concordia University, Montreal, Canada

    Yiming Xiao & Hassan Rivaz

  3. Department of Circulation and Medical Imaging, Norwegian University of Science and Technology, Trondheim, Norway

    Live Eikenes

  4. Department of Medical Technology, SINTEF, Trondheim, Norway

    Ingerid Reinertsen

  5. Norwegian National Advisory Unit for Ultrasound and Image-Guided Therapy, St. Olavs University Hospital, Trondheim, Norway

    Ingerid Reinertsen

Authors
  1. Yiming Xiao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Live Eikenes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ingerid Reinertsen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hassan Rivaz
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yiming Xiao.

Ethics declarations

Conflict of interest

Yiming Xiao, Live Eikenes, Ingerid Reinertsen, and Hassan Rivaz declare that they have no conflict of interest.

Ethical standard

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 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all participants included in the study.

Additional information

A correction to this article is available online at https://doi.org/10.1007/s11548-018-1706-x.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Xiao, Y., Eikenes, L., Reinertsen, I. et al. Nonlinear deformation of tractography in ultrasound-guided low-grade gliomas resection. Int J CARS 13, 457–467 (2018). https://doi.org/10.1007/s11548-017-1699-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-017-1699-x

Keywords

Search

Navigation