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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

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Abstract

We investigate the correlation between diffusion tensor imaging (DTI)-derived metric statistics and motor strength grade of insular glioma patients after optimizing the pyramidal tract (PT) delineation. Motor strength grades of 45 insular glioma patients were assessed. All the patients underwent structural and diffusion MRI examination before and after surgery. We co-registered pre- and post-op datasets, and a two-tensor unscented Kalman filter (UKF) algorithm was employed to delineate bilateral PTs after DWI pre-processing. The tractography results were voxelized, and their labelmaps were cropped according to the location of frontal and insular parts of the lesion. Both the whole and cropped labelmaps were used as regions of interest to analyze fractional anisotropy (FA) and Trace statistics; hence, their ratios were calculated (lesional side tract/contralateral normal tract). The combination of DWI pre-processing and two-tensor UKF algorithm successfully delineated bilateral PTs of all the patients. It effectively accomplished both full fiber delineation within the edema and an extensive lateral fanning that had a favorable correspondence to the bilateral motor cortices. Before surgery, correlations were found between patients’ motor strength grades and ratios of PT volume and FA standard deviation (SD). Nearly 3 months after surgery, correlations were found between motor strength grades and the ratios of metric statistics as follows: whole PT volume, whole mean FA, and FA SD. We substantiated the correlation between DTI-derived metric statistics and motor strength grades of insular glioma patients. Moreover, we posed a workflow for comprehensive pre- and post-op DTI quantitative research of glioma patients.

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References

  1. Sanai N, Polley M-Y, Berger MS: Insular glioma resection: assessment of patient morbidity, survival, and tumor progression. Journal of neurosurgery 112:1-9, 2010

    Article  PubMed  Google Scholar 

  2. Wang F, Sun T, Li X, Xia H, Li Z: Microsurgical and tractographic anatomical study of insular and transsylvian transinsular approach. Neurological Sciences 32:865-874, 2011

    Article  PubMed  PubMed Central  Google Scholar 

  3. Duffau H: A personal consecutive series of surgically treated 51 cases of insular WHO Grade II glioma: advances and limitations. Journal of neurosurgery 110:696-708, 2009

    Article  PubMed  Google Scholar 

  4. 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, 2007

    Article  PubMed  Google Scholar 

  5. Black P, Jolesz Fa, Medani K: From vision to reality: the origins of intraoperative MR imaging. Acta neurochirurgica Supplement 109:3-7, 2011

    Article  PubMed  Google Scholar 

  6. Chen X, Meng X, Zhang J, Wang F, Zhao Y, Xu BN: Low-grade insular glioma resection with 1.5T intra-operative MRI: preliminary results of a prospective randomized trial. Neuro-Oncology 13:iii157-iii157, 2011

  7. Dimou S, Battisti Ra, Hermens DF, Lagopoulos J: A systematic review of functional magnetic resonance imaging and diffusion tensor imaging modalities used in presurgical planning of brain tumour resection. Neurosurgical Review 36:205-214, 2013

    Article  CAS  PubMed  Google Scholar 

  8. Raabe A, Beck J, Schucht P, Seidel K: Continuous dynamic mapping of the corticospinal tract during surgery of motor eloquent brain tumors: evaluation of a new method. Journal of neurosurgery 120:1015-1024, 2014

    Article  PubMed  Google Scholar 

  9. Field AS, Alexander AL: Diffusion tensor imaging in cerebral tumor diagnosis and therapy. Topics in Magnetic Resonance Imaging 15:315-324, 2004

    Article  PubMed  Google Scholar 

  10. Laundre BJ, Jellison BJ, Badie B, Alexander AL, Field AS: Diffusion tensor imaging of the corticospinal tract before and after mass resection as correlated with clinical motor findings: preliminary data. American Journal of Neuroradiology 26:791-796, 2005

    PubMed  PubMed Central  Google Scholar 

  11. Helton KJ, et al.: Diffusion tensor imaging of tract involvement in children with pontine tumors. American Journal of Neuroradiology 27:786-793, 2006

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Lazar M, Alexander aL, Thottakara PJ, Badie B, Field aS: White matter reorganization after surgical resection of brain tumors and vascular malformations. American Journal of Neuroradiology 27:1258-1271, 2006

    CAS  PubMed  PubMed Central  Google Scholar 

  13. Kovanlikaya I, et al.: Assessment of the corticospinal tract alterations before and after resection of brainstem lesions using diffusion tensor imaging (DTI) and tractography at 3 T. European Journal of Radiology 77:383-391, 2011

    Article  PubMed  Google Scholar 

  14. Giussani C, et al.: DTI fiber tracking to differentiate demyelinating diseases from diffuse brain stem glioma. Neuroimage 52:217-223, 2010

    Article  PubMed  Google Scholar 

  15. Lindenberg R, Renga V, Zhu LL, Betzler F, Alsop D, Schlaug G: Structural integrity of corticospinal motor fibers predicts motor impairment in chronic stroke. Neurology 74:280-287, 2010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Radlinska B, Ghinani S, Leppert IR, Minuk J, Pike GB, Thiel A: Diffusion tensor imaging, permanent pyramidal tract damage, and outcome in subcortical stroke. Neurology 75:1048-1054, 2010

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Coupar F, Pollock a, Rowe P, Weir C, Langhorne P: Predictors of upper limb recovery after stroke: a systematic review and meta-analysis. Clinical Rehabilitation 26:291-313, 2012

    Article  PubMed  Google Scholar 

  18. Vargas P, et al.: Assessment of corticospinal tract (CST) damage in acute stroke patients: comparison of tract-specific analysis versus segmentation of a CST template. Journal of Magnetic Resonance Imaging 37:836-845, 2013

    Article  PubMed  Google Scholar 

  19. Groisser BN, Copen Wa, Singhal AB, Hirai KK, Schaechter JD: Corticospinal tract diffusion abnormalities early after stroke predict motor outcome. Neurorehabilitation and neural repair:1545968314521896--1545968314521896-, 2014

  20. Heiss W-D, Kidwell CS: Imaging for prediction of functional outcome and assessment of recovery in ischemic stroke. Stroke; a journal of cerebral circulation 45:1195-1201, 2014

    Article  PubMed Central  Google Scholar 

  21. Mattes D, Haynor DR, Vesselle H, Lewellen TK, Eubank W: PET-CT image registration in the chest using free-form deformations. IEEE Transactions on Medical Imaging 22:120-128, 2003

    Article  PubMed  Google Scholar 

  22. Fedorov A, et al.: 3D Slicer as an image computing platform for the quantitative imaging network. Magnetic Resonance Imaging 30:1323-1341, 2012

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wu M, et al.: Comparison of EPI distortion correction methods in diffusion tensor MRI using a novel framework. Medical image computing and computer-assisted intervention: MICCAI 11:321-329, 2008

    CAS  PubMed  Google Scholar 

  24. Pierpaoli C, et al.: TORTOISE: an integrated software package for processing of diffusion MRI data. ISMRM Annual Meeting Proceedings 18:1597-1597, 2010

    Google Scholar 

  25. Salvador R, Peña A, Menon DK, Carpenter TA, Pickard JD, Bullmore ET: Formal characterization and extension of the linearized diffusion tensor model. Human Brain Mapping 24:144-155, 2005

    Article  PubMed  Google Scholar 

  26. 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 

  27. Malcolm JG, Shenton ME, Rathi Y: Filtered multitensor tractography. IEEE Transactions on Medical Imaging 29:1664-1675, 2010

    Article  PubMed  PubMed Central  Google Scholar 

  28. Chen Z, et al.: Reconstruction of the arcuate fasciculus for surgical planning in the setting of peritumoral edema using two-tensor unscented Kalman filter tractography. NeuroImage: Clinical 7:815–822, 2015

  29. Stadlbauer A, et al.: Diffusion tensor imaging and optimized fiber tracking in glioma patients: histopathologic evaluation of tumor-invaded white matter structures. NeuroImage 34:949-956, 2007

    Article  PubMed  Google Scholar 

  30. Stadlbauer A, et al.: Detection of tumour invasion into the pyramidal tract in glioma patients with sensorimotor deficits by correlation of 18F-fluoroethyl-L- tyrosine PET and magnetic resonance diffusion tensor imaging. Acta Neurochirurgica 151:1061-1069, 2009

    Article  PubMed  Google Scholar 

  31. Verde AR, et al.: UNC-Utah NA-MIC framework for DTI fiber tract analysis. Frontiers in neuroinformatics 7:51-51, 2014

    Article  PubMed  PubMed Central  Google Scholar 

  32. Neuloh G, Pechstein U, Schramm J: Motor tract monitoring during insular glioma surgery. Journal of neurosurgery 106:582-592, 2007

    Article  PubMed  Google Scholar 

  33. Ng WH, Cheong DLH, Khu KJ, Venkatesh G, Ng YK, Lim CCT: Diffusion tensor tractography: corticospinal tract fiber reduction is associated with temporary hemiparesis in benign extracerebral lesions. Neurosurgery 63:452-458, 2008

    Article  PubMed  Google Scholar 

  34. Prabhu SP, et al.: DTI assessment of the brainstem white matter tracts in pediatric BSG before and after therapy: a report from the pediatric brain tumor consortium. Child's Nervous System 27:11-18, 2011

    Article  PubMed  Google Scholar 

  35. Ulrich NH, et al.: Brainstem cavernoma surgery with the support of pre- and postoperative diffusion tensor imaging: initial experiences and clinical course of 23 patients. Neurosurgical Review 37:481-492, 2014

    Article  PubMed  Google Scholar 

  36. Zolal A, et al.: The use of diffusion tensor images of the corticospinal tract in intrinsic brain tumor surgery: a comparison with direct subcortical stimulation. Neurosurgery 71:331-340, 2012

    Article  PubMed  Google Scholar 

  37. Nimsky C: Fiber tracking-we should move beyond diffusion tensor imaging. World neurosurgery 82:35-36, 2013

    Article  PubMed  Google Scholar 

  38. O'Donnell LJ, Pasternak O: Does diffusion MRI tell us anything about the white matter? An overview of methods and pitfalls. Schizophrenia Research 161:133-141, 2015

    Article  PubMed  Google Scholar 

  39. Pierpaoli C, et al.: TORTOISE: an integrated software package for processing of diffusion MRI data. Proc. ISMRM 18th annual meeting: City

  40. Leemans A, Jones DK: The B-matrix must be rotated when correcting for subject motion in DTI data. Magnetic Resonance in Medicine 61:1336-1349, 2009

    Article  PubMed  Google Scholar 

  41. Rohde GK, Barnett AS, Basser PJ, Marenco S, Pierpaoli C: Comprehensive approach for correction of motion and distortion in diffusion-weighted MRI. Magnetic Resonance in Medicine 51:103-114, 2004

    Article  CAS  PubMed  Google Scholar 

  42. Lienhard S, Malcolm JG, Westin C-F, Rathi Y: A full bi-tensor neural tractography algorithm using the unscented Kalman filter. EURASIP Journal on Advances in Signal Processing 2011:77-77, 2011

    Google Scholar 

  43. Cheng G, Salehian H, Forder JR, Vemuri BC: Tractography From HARDI using an intrinsic unscented kalman filter. IEEE Transactions on Medical Imaging 34:298-305, 2015

    Article  PubMed  Google Scholar 

  44. Goebell E, Paustenbach S, Vaeterlein O: Low-grade and anaplastic gliomas: differences in architecture evaluated with diffusion-tensor mr imaging. Radiology 239:217-222, 2006

    Article  PubMed  Google Scholar 

  45. Shah R, et al.: Radiation necrosis in the brain: imaging features and differentiation from tumor recurrence. Radiographics 32:1343-1359, 2012

    Article  PubMed  Google Scholar 

  46. Yeo SS, Jang SH: Differences between the somatotopic corticospinal tract for the fingers and toes in the human brain. NeuroRehabilitation 31:395-399, 2012

    Article  PubMed  Google Scholar 

  47. Fudaba H, et al.: Comparison of multiple parameters obtained on 3T pulsed arterial spin-labeling, diffusion tensor imaging, and MRS and the Ki-67 labeling index in evaluating glioma grading. AJNR American journal of neuroradiology 35:14-18, 2014

    Article  Google Scholar 

  48. Huston JM, Field AS: Clinical applications of diffusion tensor imaging. Magnetic Resonance Imaging Clinics of North America 21:279-298, 2013

    Article  PubMed  Google Scholar 

  49. Jeong JW, Asano E, Juhász C, Chugani HT: Quantification of primary motor pathways using diffusion MRI tractography and its application to predict postoperative motor deficits in children with focal epilepsy. Human Brain Mapping 35:3216-3226, 2014

    Article  PubMed  Google Scholar 

  50. Alexander aL, Lee JE, Lazar M, Field aS: Diffusion tensor imaging of the brain. Neurotherapeutics : the journal of the American Society for Experimental NeuroTherapeutics 4:316-329, 2007

    Google Scholar 

  51. Boespflug EL, Storrs JM, Allendorfer JB, Lamy M, Eliassen JC, Page S: Mean diffusivity as a potential diffusion tensor biomarker of motor rehabilitation after electrical stimulation incorporating task specific exercise in stroke: a pilot study. Brain Imaging and Behavior:1–11, 2011

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Funding

This work was supported by the National Natural Science Foundation of China (81701796) and the China Scholarship Council (CSC No. 201406200059).

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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 & Jie Tang

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

    Qiongge Li & Jie Lu

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  1. Ye Li
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  3. Qiongge Li
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Correspondence to Jie Tang or Jie Lu.

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Li, Y., Hou, Y., Li, Q. et al. 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). https://doi.org/10.1007/s10278-021-00578-4

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  • DOI: https://doi.org/10.1007/s10278-021-00578-4

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