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Assessment of acute traumatic cervical spinal cord injury using conventional magnetic resonance imaging in combination with diffusion tensor imaging–tractography: a retrospective comparative study

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Abstract

Purpose

The application of conventional magnetic resonance imaging (MRI) in combination with diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) to diagnose acute traumatic cervical SCI has not been studied. This study explores the role of MRI with DTI-DTT in the diagnosis of acute traumatic cervical spinal cord injury (SCI).

Methods

Thirty patients with acute traumatic cervical SCI underwent conventional MRI and DTI-DTT. Conventional MRI was used to detect the intramedullary lesion length (IMLL) and intramedullary hemorrhage length (IMHL). DTI was used to detect the spinal cord’s fractional anisotropy (FA) and apparent diffusion coefficient value, and DTT detected the imaginary white matter fiber volume and the connection rates of fiber tractography (CRFT). Patients’ neurological outcome was determined using the American Spinal Injury Association (ASIA) Impairment Scale (AIS) grades.

Results

Patients were divided into group A (without AIS grade conversion) and group B (with AIS grade conversion). The IMLL and IMHL of group A were significantly higher than those of group B. The FA and CRFT of group A were significantly lower than those of group B. The final AIS grade was negatively correlated with the IMLL and IMHL, and positively correlated with the FA and CRFT. According to imaging features based on conventional MRI and DTI-DTT, we propose a novel classification and diagnostic procedure.

Conclusions

The combination of conventional MRI with DTI-DTT is a valid diagnostic approach for SCI. Lower IMLL and IMHL, and higher FA value and CRFT are linked to better neurological outcomes.

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References

  1. David G, Mohammadi S, Martin AR et al (2019) Traumatic and nontraumatic spinal cord injury: pathological insights from neuroimaging. Nat Rev Neurol 15:718–731

    Article  PubMed  Google Scholar 

  2. Freund P, Seif M, Weiskopf N et al (2019) MRI in traumatic spinal cord injury: from clinical assessment to neuroimaging biomarkers. Lancet Neurol 18:1123–1135

    Article  PubMed  Google Scholar 

  3. Talbott JF, Whetstone WD, Readdy WJ et al (2015) The brain and spinal injury center score: a novel, simple, and reproducible method for assessing the severity of acute cervical spinal cord injury with axial T2-weighted MRI fndings. J Neurosurg Spine 23:495–504

    Article  PubMed  Google Scholar 

  4. Andreoli C, Colaiacomo MC, Beccaglia MR et al (2005) MRI in the acute phase of spinal cord traumatic lesions: relationship between MRI findings and neurological outcome. Radiol Med 110:636–45

    PubMed  Google Scholar 

  5. Farhadi HF, Kukreja S, Minnema A et al (2018) Impact of admission imaging fndings on neurological outcomes in acute cervical traumatic spinal cord injury. J Neurotrauma 35:1398–406

    Article  PubMed  Google Scholar 

  6. Aarabi B, Sansur CA, Ibrahimi DM et al (2017) intramedullary lesion length on postoperative magnetic resonance imaging is a strong predictor of ASIA impairment scale grade conversion following decompressive surgery in cervical spinal cord injury. Neurosurgery 80:610–620

    Article  PubMed  Google Scholar 

  7. Dalkilic T, Fallah N, Noonan VK et al (2018) Predicting injury severity and neurological recovery after acute cervical spinal cord injury: a comparison of cerebrospinal fluid and magnetic resonance imaging biomarkers. J Neurotrauma 35:435–45

    Article  PubMed  Google Scholar 

  8. Le E, Aarabi B, Hersh DS et al (2015) Predictors of intramedullary lesion expansion rate on MR images of patients with subaxial spinal cord injury. J Neurosurg Spine 22:611–21

    Article  PubMed  Google Scholar 

  9. Furlan JC, Kailaya-Vasan A, Aarabi B et al (2011) A novel approach to quantitatively assess posttraumatic cervical spinal canal compromise and spinal cord compression: a multicenter responsiveness study. Spine 36:784–93

    Article  PubMed  Google Scholar 

  10. Miyanji F, Furlan JC, Aarabi B et al (2007) Acute cervical traumatic spinal cord injury: MR imaging findings correlated with neurologic outcome—prospective study with 100 consecutive patients. Radiology 243:820–7

    Article  PubMed  Google Scholar 

  11. Wang K, Chen Z, Zhang F et al (2017) Evaluation of DTI parameter ratios and diffusion tensor tractography grading in the diagnosis and prognosis prediction of cervical spondylotic myelopathy. Spine 42:E202–E210

    Article  PubMed  Google Scholar 

  12. Yung A, Mattucci S, Bohnet B et al (2019) Diffusion tensor imaging shows mechanism-specific differences in injury pattern and progression in rat models of acute spinal cord injury. Neuroimage 186:43–55

    Article  PubMed  Google Scholar 

  13. Li XH, Li JB, He XJ et al (2015) Timing of diffusion tensor imaging in the acute spinal cord injury of rats. Sci Rep 5:12639

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Thurnher MM, Law M (2009) Diffusion-weighted imaging, diffusion-tensor imaging, and fiber tractography of the spinal cord. Magn Reson Imaging Clin N Am 17:225–44

    Article  PubMed  Google Scholar 

  15. Takano M, Komaki Y, Hikishima K et al (2013) In vivo tracing of neural tracts in tiptoe walking yoshimura mice by diffusion tensor tractography. Spine 38(2):E66–E72. https://doi.org/10.1097/BRS.0b013e31827aacc2

    Article  PubMed  Google Scholar 

  16. Cohen Y, Anaby D, Morozov D (2017) Diffusion MRI of the spinal cord: from structural studies to pathology: microstructure and pathology in the spinal cord by diffusion mrI. NMR Biomed 30(3):e3592. https://doi.org/10.1002/nbm.3592

    Article  Google Scholar 

  17. Yokohama T, Iwasaki M, Oura D et al (2019) The reliability of reduced field-of-view DTI for highly accurate quantitative assessment of cervical spinal cord tracts. Magn Reson Med Sci 18:36–43

    Article  CAS  PubMed  Google Scholar 

  18. Budzik JF, Balbi V, Thuc VL et al (2011) Diffusion tensor imaging and fibre tracking in cervical spondylotic myelopathy. Eur Radiol 21:426–33

    Article  PubMed  Google Scholar 

  19. Zaninovich OA, Avila MJ, Kay M et al (2019) (2019) The role of diffusion tensor imaging in the diagnosis, prognosis, and assessment of recovery and treatment of spinal cord injury: a systematic review. Neurosurg Focus 46:E7

    Article  PubMed  Google Scholar 

  20. Chang Y, Jung TD, Yoo DS et al (2010) Diffusion tensor imaging and fiber tractography of patients with cervical spinal cord injury. J Neurotrauma 27:2033–2040

    Article  PubMed  Google Scholar 

  21. Mukherjee P, Berman JI, Chung SW et al (2008) Diffusion tensor MR imaging and fiber tractography: theoretic underpinnings. AJNR Am J Neuroradiol 29:632–641

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Fujiyoshi K, Konomi T, Yamada M et al (2013) Diffusion tensor imaging and tractography of the spinal cord: from experimental studies to clinical application. Exp Neurol 242:74–82

    Article  PubMed  Google Scholar 

  23. Cui JL, Li X, Chan TY et al (2015) Quantitative assessment of column-specific degeneration in cervical spondylotic myelopathy based on diffusion tensor tractography. Eur Spine J 24:41–7

    Article  PubMed  Google Scholar 

  24. Shabani S, Kaushal M, Budde M et al (2019) Correlation of magnetic resonance diffusion tensor imaging parameters with American spinal injury association score for prognostication and long-term outcomes. Neurosurg Focus 46:E2

    Article  PubMed  Google Scholar 

  25. Nouri A, Tetreault L, Dalzell K et al (2017) (2017) The relationship between preoperative clinical presentation and quantitative magnetic resonance imaging features in patients with degenerative cervical myelopathy. Neurosurgery 80:121–128

    Article  PubMed  Google Scholar 

  26. D’souza MM, Choudhary A, Poonia M et al (2017) Diffusion tensor MR imaging in spinal cord injury. Injury 48:880–884

    Article  PubMed  Google Scholar 

  27. Poplawski MM, Alizadeh M, Oleson CV et al (2019) Application of diffusion tensor imaging in forecasting neurological injury and recovery after human cervical spinal cord injury. J Neurotrauma 36:3051–3061

    Article  PubMed  Google Scholar 

  28. Mulcahey MJ, Samdani A, Gaughan J et al (2012) Diffusion tensor imaging in pediatric spinal cord injury: preliminary examination of reliability and clinical correlation. Spine 37(13):E797–E803. https://doi.org/10.1097/BRS.0b013e3182470a08

    Article  CAS  PubMed  Google Scholar 

  29. Koskinen EA, Hakulinen U, Brander AE et al (2014) Clinical correlates of cerebral diffusion tensor imaging findings in chronic traumatic spinal cord injury. Spinal Cord 52:202–8

    Article  CAS  PubMed  Google Scholar 

  30. Chiang CW, Wang Y, Sun P et al (2014) Quantifying white matter tract diffusion parameters in the presence of increased extra-fber cellularity and vasogenic edema. Neuroimage 101:310–319

    Article  PubMed  Google Scholar 

  31. Koskinen E, Brander A, Hakulinen U et al (2013) Assessing the State of Chronic Spinal Cord Injury Using Diffusion Tensor Imaging. J Neurotrauma 30:1587–95

    Article  PubMed  Google Scholar 

  32. Maki S, Koda M, Ota M et al (2018) Reduced field-of-view diffusion tensor imaging of the spinal cord shows motor dysfunction of the lower extremities in patients with cervical compression myelopathy. Spine 43(2):89–96. https://doi.org/10.1097/BRS.0000000000001123

    Article  PubMed  Google Scholar 

  33. Lindberg PG, Sanchez K, Ozcan F et al (2016) Correlation of force control with regional spinal DTI in patients with cervical spondylosis without signs of spinal cord injury on conventional MRI. Eur Radiol 26:733–42

    Article  PubMed  Google Scholar 

  34. Jirjis MB, Valdez C, Vedantam A et al (2017) Diffusion tensor imaging as a biomarker for assessing neuronal stem cell treatments affecting areas distal to the site of spinal cord injury. J Neurosurg Spine 26:243–251

    Article  PubMed  Google Scholar 

  35. Saksena S, Mohamed FB, Middleton DM et al (2019) DTI Assessment of regional white matter changes in the cervical and thoracic spinal cord in pediatric subjects. J Neurotrauma 36:853–861

    Article  PubMed  PubMed Central  Google Scholar 

  36. Vedantam A, Jirjis MB, Schmit BD et al (2014) Diffusion tensor imaging of the spinal cord: insights from animal and human studies. Neurosurgery 74:1–8

    Article  PubMed  Google Scholar 

  37. Middleton DM, Mohamed FB, Barakat N et al (2014) An investigation of motion correction algorithms for pediatric spinal cord DTI in healthy subjects and patients with spinal cord injury. Magn Reson Imaging 32:433–439

    Article  PubMed  Google Scholar 

  38. Sun P, Murphy RKJ, Gamble P et al (2017) Diffusion assessment of cortical changes, induced by traumatic spinal cord injury. Brain Sci 7:21

    Article  PubMed Central  CAS  Google Scholar 

  39. Rutman AM, Peterson DJ, Cohen WA et al (2018) Diffusion tensor imaging of the spinal cord: clinical value, investigational applications, and technical limitations. Curr Probl Diagn Radiol 47:257–269

    Article  PubMed  Google Scholar 

  40. 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

    Article  CAS  PubMed  Google Scholar 

  41. Wang Y, Wang Q, Haldar JP et al (2011) Quantifcation of increased cellularity during inflammatory demyelination. Brain 134:3590–3601

    Article  PubMed  Google Scholar 

  42. Chan T-Y, Li X, Mak K-C et al (2015) Normal values of cervical spinal cord diffusion tensor in young and middle-aged healthy Chinese. Eur Spine J 24:2991–8

    Article  PubMed  Google Scholar 

  43. Zhu F, Liu Y, Zeng L et al (2021) Evaluating the severity and prognosis of acute traumatic cervical spinal cord injury: a novel classification using diffusion tensor imaging and diffusion tensor tractography. Spine 46(10):687–694. https://doi.org/10.1097/BRS.0000000000003923

    Article  PubMed  Google Scholar 

  44. Zhu F, Zeng L, Gui S et al (2021) The role of diffusion tensor imaging and diffusion tensor tractography in the assessment of acute traumatic thoracolumbar spinal cord injury. World Neurosug 150:e23–e30

    Article  Google Scholar 

  45. Kirshblum SC, Burns SP, Biering-Sorensen F et al (2011) International standards for neurological classification of spinal cord injury (Revised 2011). J Spinal Cord Med 34:535–46

    Article  PubMed  PubMed Central  Google Scholar 

  46. Zhu F, Yao S, Ren Z et al (2019) Early durotomy with duroplasty for severe adult spinal cord injury without radiographic abnormality: a novel concept and method of surgical decompression. Eur Spine J 28:2275–2282

    Article  PubMed  Google Scholar 

  47. Badhiwala JH, Ahuja CS, Fehlings MG (2018) Time is spine: a review of translational advances in spinal cord injury. J Neurosurg Spine 30:1–18

    Article  PubMed  Google Scholar 

  48. Jug M, Kejžar N, Cimerman M et al (2019) Window of opportunity for surgical decompression in patients with acute traumatic cervical spinal cord injury. J Neurosurg Spine 27:1–9

    Google Scholar 

  49. Badhiwala JH, Wilson JR, Witiw CD et al (2021) The influence of timing of surgical decompression for acute spinal cord injury: a pooled analysis of individual patient data. Lancet Neurol 20:117–126

    Article  CAS  PubMed  Google Scholar 

  50. Rutges JPHJ, Kwon BK et al (2017) A prospective serial MRI study following acute traumatic cervical spinal cord injury. Eur Spine J 26:2324–2332

    Article  PubMed  Google Scholar 

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Funding

This study was supported by National Natural Science Foundation of China (81873999, 81672158).

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Correspondence to Xiaodong Guo.

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Zhu, F., Wang, Y., Kong, X. et al. Assessment of acute traumatic cervical spinal cord injury using conventional magnetic resonance imaging in combination with diffusion tensor imaging–tractography: a retrospective comparative study. Eur Spine J 31, 1700–1709 (2022). https://doi.org/10.1007/s00586-022-07207-w

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  • DOI: https://doi.org/10.1007/s00586-022-07207-w

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