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Quantitative Validation of the Correlation Between Optimized Pyramidal Tract Delineation After Brain Shift Compensation and Direct Electrical Subcortical Stimulation During Brain Tumor Surgery

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Abstract

It remains unclear whether tractography of pyramidal tracts is correlated with the intraoperative direct electrical subcortical stimulation (DESS), and brain shift further complicates the issue. The objective of this research is to quantitatively verify the correlation between optimized tractography (OT) of pyramidal tracts after brain shift compensation and DESS during brain tumor surgery. OT was performed for 20 patients with lesions in proximity to the pyramidal tracts based on preoperative diffusion-weighted magnetic resonance imaging. During surgery, tumor resection was guided by DESS. A total of 168 positive stimulation points and their corresponding stimulation intensity thresholds were recorded. Using the brain shift compensation algorithm based on hierarchical B-spline grids combined with a Gaussian resolution pyramid, we warped the preoperative pyramidal tract models and used receiver operating characteristic (ROC) curves to investigate the reliability of our brain shift compensation method based on anatomic landmarks. Additionally, the minimum distance between the DESS points and warped OT (wOT) model was measured and correlated with DESS intensity threshold. Brain shift compensation was achieved in all cases, and the area under the ROC curve was 0.96 in the registration accuracy analysis. The minimum distance between the DESS points and the wOT model was found to have a significantly high correlation with the DESS stimulation intensity threshold (r = 0.87, P < 0.001), with a linear regression coefficient of 0.96. Our OT method can provide comprehensive and accurate visualization of the pyramidal tracts for neurosurgical navigation and was quantitatively verified by intraoperative DESS after brain shift compensation.

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Data Availability

The in-house datasets may not be made publically available due to the hospital internal regulations of clinical data usage.

References

  1. Nimsky C, Bauer M, Carl B: Merits and limits of tractography techniques for the uninitiated. Adv Tech Stand Neurosurg 37–60, 2016

  2. Wende T, Hoffmann KT, Meixensberger J: Tractography in neurosurgery: A systematic review of current applications. J Neurol Surg A Cent Eur Neurosurg 81:442-455, 2020

    Article  PubMed  Google Scholar 

  3. Chen Z, et al.: 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, 2016

    Article  PubMed  PubMed Central  Google Scholar 

  4. Pierpaoli C, et al.: TORTOISE: an integrated software package for processing of diffusion MRI data. ISMRM 18th Annual Meeting, 2010

  5. Roder C, Haas P, Tatagiba M, Ernemann U, Bender B: Technical limitations and pitfalls of diffusion-weighted imaging in intraoperative high-field MRI. Neurosurg Rev 44:327-334, 2021

    Article  PubMed  Google Scholar 

  6. Li Y, Hou Y, Li Q, Tang J, Lu J: Optimized tractography mapping and quantitative evaluation of pyramidal tracts for surgical resection of insular gliomas: A correlative study with diffusion tensor imaging-derived metrics and patient motor strength. J Digit Imaging 35:356-364, 2022

    Article  PubMed  PubMed Central  Google Scholar 

  7. Shiban E, et al.: Intraoperative subcortical motor evoked potential stimulation: how close is the corticospinal tract? J Neurosurg 123:711-720, 2015

    Article  PubMed  Google Scholar 

  8. Berger MS, Ojemann GA: Intraoperative brain mapping techniques in neuro-oncology. Stereotact Funct Neurosurg 58:153-161, 1992

    Article  CAS  PubMed  Google Scholar 

  9. Javadi SA, Nabavi A, Giordano M, Faghihzadeh E, Samii A: Evaluation of diffusion tensor imaging–based tractography of the corticospinal tract: A correlative study with intraoperative magnetic resonance imaging and direct electrical subcortical stimulation. Neurosurgery 80:287-299, 2017

    Article  PubMed  Google Scholar 

  10. Ostry S, Belsan T, Otahal J, Benes V, Netuka D: Is intraoperative diffusion tensor imaging at 3.0T comparable to subcortical corticospinal tract mapping? Neurosurgery 73:797–807; discussion 806–797, 2013

  11. Munnich T, et al.: Tractography verified by intraoperative magnetic resonance imaging and subcortical stimulation during tumor resection near the corticospinal tract. Oper Neurosurg 16:197-210, 2019

    Article  Google Scholar 

  12. Mandelli ML, Berger MS, Bucci M, Berman JI, Amirbekian B, Henry RG: Quantifying accuracy and precision of diffusion MR tractography of the corticospinal tract in brain tumors. J Neurosurg 121:349-358, 2014

    Article  PubMed  Google Scholar 

  13. Liu Y, et al.: An ITK implementation of a physics-based non-rigid registration method for brain deformation in image-guided neurosurgery. Front Neuroinform 8:33, 2014

    Article  PubMed  PubMed Central  Google Scholar 

  14. Jones DK, Cercignani M: Twenty-five pitfalls in the analysis of diffusion MRI data. NMR Biomed 23:803-820, 2010

    Article  PubMed  Google Scholar 

  15. Ozawa N, Muragaki Y, Nakamura R, Lseki H: Intraoperative diffusion-weighted imaging for visualization of the pyramidal tracts. Part I: pre-clinical validation of the scanning protocol. Minim Invasive Neurosurg 51:63–66, 2008

  16. Chen X, Weigel D, Ganslandt O, Fahlbusch R, Buchfelder M, Nimsky C: Diffusion tensor-based fiber tracking and intraoperative neuronavigation for the resection of a brainstem cavernous angioma. Surg Neurol 68:285–291; discussion 291, 2007

  17. Tokuda J, et al.: OpenIGTLink: an open network protocol for image-guided therapy environment. Int J Med Robot 5:423-434, 2009

    Article  PubMed  PubMed Central  Google Scholar 

  18. Lee S, Woberg G, Chwa KY, Shin SY: Image metamorphosis with scattered feature constraints. IEEE Trans Vis Comput Graph, 1996

  19. Shamonin DP, et al.: Fast parallel image registration on CPU and GPU for diagnostic classification of Alzheimer's disease. Front Neuroinform 7:50, 2013

    Article  PubMed  Google Scholar 

  20. Xie Z, Farin GE: Image registration using hierarchical B-splines. IEEE Trans Vis Comput Graph 10:85-94, 2004

    Article  PubMed  Google Scholar 

  21. Hinkle D: Applied Statistics for the Behavioral Sciences. J Educ Behav Stat 15, 2003

  22. Zhang F, et al.: Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 249:118870, 2022

    Article  PubMed  Google Scholar 

  23. Wu JS, et al.: Clinical evaluation and follow-up outcome of diffusion tensor imaging-based functional neuronavigation: a prospective, controlled study in patients with gliomas involving pyramidal tracts. Neurosurgery 61:935–948; discussion 948–939, 2007

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

    Article  PubMed  Google Scholar 

  25. Costabile JD, Alaswad E, D'Souza S, Thompson JA, Ormond DR: Current applications of diffusion tensor imaging and tractography in intracranial tumor resection. Front Oncol 9:426, 2019

    Article  PubMed  PubMed Central  Google Scholar 

  26. Nossek E, et al.: Intraoperative mapping and monitoring of the corticospinal tracts with neurophysiological assessment and 3-dimensional ultrasonography-based navigation. Clinical article. J Neurosurg 114:738-746, 2011

    Article  PubMed  Google Scholar 

  27. Yang JY, et al.: Assessment of intraoperative diffusion EPI distortion and its impact on estimation of supratentorial white matter tract positions in pediatric epilepsy surgery. Neuroimage Clin 35:103097, 2022

    Article  PubMed  PubMed Central  Google Scholar 

  28. Mandonnet E, Winkler PA, Duffau H: Direct electrical stimulation as an input gate into brain functional networks: principles, advantages and limitations. Acta Neurochir (Wien) 152:185-193, 2010

    Article  PubMed  Google Scholar 

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Funding

This work was supported by the National Natural Science Foundation of China (81701796) and the Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund Project (L222022).

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Authors and Affiliations

  1. Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China

    Ye Li, Yuanzheng Hou, Xiaoyu Li & Jie Tang

  2. Department of Radiology, Xuanwu Hospital, Capital Medical University, 45 Changchun Street, Xicheng District, Beijing, 100853, China

    Qiongge Li & Jie Lu

Authors
  1. Ye Li
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  2. Yuanzheng Hou
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  3. Xiaoyu Li
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  4. Qiongge Li
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  5. Jie Lu
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  6. Jie Tang
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Corresponding authors

Correspondence to Jie Lu or Jie Tang.

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Ethical Approval and Consent to Participate

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. All patients provided written informed consent, and the institutional review board of the hospital approved this study.

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The authors declare no competing interests.

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Li, Y., Hou, Y., Li, X. et al. Quantitative Validation of the Correlation Between Optimized Pyramidal Tract Delineation After Brain Shift Compensation and Direct Electrical Subcortical Stimulation During Brain Tumor Surgery. J Digit Imaging 36, 1974–1986 (2023). https://doi.org/10.1007/s10278-023-00867-0

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  • DOI: https://doi.org/10.1007/s10278-023-00867-0

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