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Assessment of Injured Spinal Cord Using Diffusion Tensor Tractography

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Neuroprotection and Regeneration of the Spinal Cord

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Abstract

In spinal cord injury (SCI), the evaluation of axonal fibers is important to assess the severity of injury and efficacy of any treatment protocol, but conventional methods such as tracer injection in the brain parenchyma are highly invasive and require histological evaluation, precluding clinical applications. Magnetic resonance imaging (MRI) is essential for predicting prognosis and planning treatment of patients with SCI noninvasively. However, the information provided by conventional T1- and T2-weighted MRI of the spinal cord is essentially limited to the differentiation of the white matter from the gray matter. By contrast, diffusion-weighted magnetic resonance imaging (DWI) provides much information about biological structures. In particular, diffusion tensor imaging (DTI) and diffusion tensor tractography (DTT) are powerful tools for evaluating white matter fibers in the central nervous system. We previously established a reproducible SCI model in adult common marmosets and demonstrated that DTT could be used to trace the neural tracts in the intact and injured spinal cord of these animals in vivo. Recently, many reports using DTT to analyze the spinal cord area have been published. Based on the findings from our experimental studies, we are now routinely performing DTT of the human spinal cord clinically. In this chapter, we outline the basic principles of DTT and describe the characteristics, limitations, and clinical application of DTT in the spinal cord.

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Notes

  1. 1.

    According to textbooks, DWI “includes all MRI images weighted with proton diffusion motion by some method,” but in practice it is more narrowly defined as meaning the original images acquired by adding a diffusion-weighted gradient magnetic field [33].

  2. 2.

    Tensor is a function of a vector variable that possesses multilinearity, and when multiplied by the vector (on the left), it forms a matrix that yields vectors, with each component of the matrix being closely related to the coordinate system [30]. In actuality, a 3 × 3, 2-step matrix operation is necessary.

  3. 3.

    The “b value” is related to the diffusion setting in DWI. It is calculated by the following formula, in which γ (MHz) is the gyromagnetic ratio, G (mT/m) is the size of the MPG, δ (ms) is the MPG application time, and Δ (ms) is the starting time of each pair of gradient magnetic fields.

    $$ b={\gamma}^2{G_x}^2{\delta}^2\left(\varDelta -\delta /3\right) $$

Abbreviations

MRI:

Magnetic resonance imaging

DWI:

Diffusion-weighted MR imaging

DTI:

Diffusion tensor imaging

DTT:

Diffusion tensor tractography

FA:

Fractional anisotropy

ADC:

Apparent diffusion coefficient

References

  1. Iwanami A, Yamane J, Katoh H, Nakamura M, Momoshima S, Ishii H, Tanioka Y, Tamaoki N, Nomura T, Toyama Y, Okano H (2005) Establishment of graded spinal cord injury model in a nonhuman primate: the common marmoset. J Neurosci Res 80:172–181

    Article  CAS  PubMed  Google Scholar 

  2. Olson L (2002) Medicine: clearing a path for nerve growth. Nature 416:589–590

    Article  CAS  PubMed  Google Scholar 

  3. Kaneko S, Iwanami A, Nakamura M, Kishino A, Kikuchi K, Shibata S, Okano HJ, Ikegami T, Moriya A, Konishi O, Nakayama C, Kumagai K, Kimura T, Sato Y, Goshima Y, Taniguchi M, Ito M, He Z, Toyama Y, Okano H (2007) A selective Sema3A inhibitor enhances regenerative responses and functional recovery of the injured spinal cord. Nat Med 12:1380–1389

    Article  Google Scholar 

  4. Ralston DD, Ralston HJ 3rd (1985) The terminations of corticospinal tract axons in the macaque monkey. J Comp Neurol 242:325–337

    Article  CAS  PubMed  Google Scholar 

  5. Lacroix S, Havton LA, McKay H, Yang H, Brant A, Roberts J, Tuszynski MH (2004) Bilateral corticospinal projections arise from each motor cortex in the macaque monkey: a quantitative study. J Comp Neurol 473:147–161

    Article  PubMed  Google Scholar 

  6. Kulkarni MV, Williams JC, Yeakley JW, Andrews JL, McArdle CB, Narayana PA, Howell RR, Jonas AJ (1987) Magnetic resonance imaging in the diagnosis of the cranio-cervical manifestations of the mucopolysaccharidoses. Magn Reson Imaging 5:317–323

    Article  CAS  PubMed  Google Scholar 

  7. Yamashita Y, Takahashi M, Matsuno Y, Sakamoto Y, Oguni T, Sakae T, Yoshizumi K, Kim EE (1990) Chronic injuries of the spinal cord: assessment with MR imaging. Radiology 175: 849–854

    CAS  PubMed  Google Scholar 

  8. Iwanami A, Kaneko S, Nakamura M, Kanemura Y, Mori H, Kobayashi S, Yamasaki M, Momoshima S, Ishii H, Ando K, Tanioka Y, Tamaoki N, Nomura T, Toyama Y, Okano H (2005) Transplantation of human neural stem cells for spinal cord injury in primates. J Neurosci Res 80:182–190

    Article  CAS  PubMed  Google Scholar 

  9. Ito R, Mori S, Melhem ER (2002) Diffusion tensor brain imaging and tractography. Neuroimaging Clin N Am 12:1–19

    Article  PubMed  Google Scholar 

  10. Masutani Y, Aoki S, Abe O, Hayashi N, Otomo K (2003) MR diffusion tensor imaging: recent advance and new techniques for diffusion tensor visualization. Eur J Radiol 46:53–66

    Article  PubMed  Google Scholar 

  11. Mori H, Masutani Y, Aoki S, Abe O, Hayashi N, Masumoto T, Yamada H, Yoshikawa T, Kunimatsu A, Ohtomo K, Kabasawa H (2003) [Simple visualization of the corticospinal pathway using tractography: one-ROI and two-ROI methods]. Nippon Igaku Hoshasen Gakkai Zasshi 63:51–53

    PubMed  Google Scholar 

  12. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M (1986) MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology 161:401–407

    PubMed  Google Scholar 

  13. Moseley ME, Cohen Y, Kucharczyk J, Mintorovitch J, Asgari HS, Wendland MF, Tsuruda J, Norman D (1990) Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 176:439–445

    CAS  PubMed  Google Scholar 

  14. Basser PJ, Mattiello J, LeBihan D (1994) MR diffusion tensor spectroscopy and imaging. Biophys J 66:259–267

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  15. Mori S, Zhang J (2006) Principles of diffusion tensor imaging and its applications to basic neuroscience research. Neuron 51:527–539

    Article  CAS  PubMed  Google Scholar 

  16. Conturo TE, Lori NF, Cull TS, Akbudak E, Snyder AZ, Shimony JS, McKinstry RC, Burton H, Raichle ME (1999) Tracking neuronal fiber pathways in the living human brain. Proc Natl Acad Sci U S A 96:10422–10427

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  17. Kamada K, Todo T, Morita A, Masutani Y, Aoki S, Ino K, Kawai K, Kirino T (2005) Functional monitoring for visual pathway using real-time visual evoked potentials and optic-radiation tractography. Neurosurgery 57:121–127, discussion 121–127

    Article  PubMed  Google Scholar 

  18. Lee JS, Han MK, Kim SH, Kwon OK, Kim JH (2005) Fiber tracking by diffusion tensor imaging in corticospinal tract stroke: topographical correlation with clinical symptoms. NeuroImage 26:771–776

    Article  PubMed  Google Scholar 

  19. Basser PJ, Jones DK (2002) Diffusion-tensor MRI: theory, experimental design and data analysis—a technical review. NMR Biomed 15:456–467

    Article  PubMed  Google Scholar 

  20. Maier SE, Mamata H (2005) Diffusion tensor imaging of the spinal cord. Ann N Y Acad Sci 1064:50–60

    Article  PubMed  Google Scholar 

  21. Kharbanda HS, Alsop DC, Anderson AW, Filardo G, Hackney DB (2006) Effects of cord motion on diffusion imaging of the spinal cord. Magn Reson Med 56:334–339

    Article  PubMed  Google Scholar 

  22. Holder CA, Muthupillai R, Mukundan S Jr, Eastwood JD, Hudgins PA (2000) Diffusion-weighted MR imaging of the normal human spinal cord in vivo. AJNR Am J Neuroradiol 21:1799–1806

    CAS  PubMed  Google Scholar 

  23. Facon D, Ozanne A, Fillard P, Lepeintre JF, Tournoux-Facon C, Ducreux D (2005) MR diffusion tensor imaging and fiber tracking in spinal cord compression. AJNR Am J Neuroradiol 26:1587–1594

    PubMed  Google Scholar 

  24. Tsuchiya K, Fujikawa A, Suzuki Y (2005) Diffusion tractography of the cervical spinal cord by using parallel imaging. AJNR Am J Neuroradiol 26:398–400

    PubMed  Google Scholar 

  25. Ducreux D, Lepeintre JF, Fillard P, Loureiro C, Tadie M, Lasjaunias P (2006) MR diffusion tensor imaging and fiber tracking in 5 spinal cord astrocytomas. AJNR Am J Neuroradiol 27:214–216

    CAS  PubMed  Google Scholar 

  26. Le Bihan D, Breton E, Lallemand D, Aubin ML, Vignaud J, Laval-Jeantet M (1988) Separation of diffusion and perfusion in intravoxel incoherent motion MR imaging. Radiology 168: 497–505

    PubMed  Google Scholar 

  27. Burdette JH, Elster AD, Ricci PE (1999) Acute cerebral infarction: quantification of spin-density and T2 shine-through phenomena on diffusion-weighted MR images. Radiology 212: 333–339

    Article  CAS  PubMed  Google Scholar 

  28. Burdette JH, Ricci PE, Petitti N, Elster AD (1998) Cerebral infarction: time course of signal intensity changes on diffusion-weighted MR images. AJR Am J Roentgenol 171:791–795

    Article  CAS  PubMed  Google Scholar 

  29. Yamashita T, Kwee TC, Takahara T (2009) Whole-body magnetic resonance neurography. N Engl J Med 361:538–539

    Article  CAS  PubMed  Google Scholar 

  30. Le Bihan D, Mangin JF, Poupon C, Clark CA, Pappata S, Molko N, Chabriat H (2001) Diffusion tensor imaging: concepts and applications. J Magn Reson Imaging 13:534–546

    Article  PubMed  Google Scholar 

  31. Fujiyoshi K, Konomi T 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 

  32. Le Bihan D, van Zijl P (2002) From the diffusion coefficient to the diffusion tensor. NMR Biomed 15:431–434

    Article  PubMed  Google Scholar 

  33. Aoki S, Abe O, Masutani Y (eds) (2005) New edition: making sense of diffusion MRI (Japanese). Shujunsha, Tokyo

    Google Scholar 

  34. Takagi T, Nakamura M, Yamada M, Hikishima K, Momoshima S, Fujiyoshi K, Shibata S, Okano H, Toyama Y, Okano H (2009) Visualization of peripheral nerve degeneration and regeneration: monitoring with diffusion tensor tractography. NeuroImage 44:884–892

    Article  PubMed  Google Scholar 

  35. Pajevic S, Pierpaoli C (2000) Color schemes to represent the orientation of anisotropic tissues from diffusion tensor data: application to white matter fiber tract mapping in the human brain. Magn Reson Med 43:921

    Article  PubMed  Google Scholar 

  36. Mori S, van Zijl PCM (2002) Fiber tracking: principles and strategies - a technical review. NMR Biomed 15:468–480

    Google Scholar 

  37. Kamada K, Todo T, Masutani Y, Aoki S, Ino K, Takano T, Kirino T, Kawahara N, Morita A (2005) Combined use of tractography-integrated functional neuronavigation and direct fiber stimulation. J Neurosurg 102:664–672

    Article  PubMed  Google Scholar 

  38. Fujiyoshi K, Yamada M, Nakamura M, Yamane J, Katoh H, Kitamura K, Kawai K, Okada S, Momoshima S, Toyama Y, Okano H (2007) In vivo tracing of neural tracts in the intact and injured spinal cord of marmosets by diffusion tensor tractography. J Neurosci 27:11991–11998

    Article  CAS  PubMed  Google Scholar 

  39. Konomi T, Fujiyoshi K et al (2012) Conditions for quantitative evaluation of injured spinal cord by in vivo diffusion tensor imaging and tractography: preclinical longitudinal study in common marmosets. NeuroImage 63:1841–1853

    Article  PubMed  Google Scholar 

  40. Cruz LC Jr, Domingues RC, Gasparetto EL (2009) Diffusion tensor imaging of the cervical spinal cord of patients with relapsing-remising multiple sclerosis: a study of 41 cases. Arq Neuropsiquiatr 67:391–395

    Article  PubMed  Google Scholar 

  41. Renoux J, Facon D, Fillard P, Huynh I, Lasjaunias P, Ducreux D (2006) MR diffusion tensor imaging and fiber tracking in inflammatory diseases of the spinal cord. AJNR Am J Neuroradiol 27:1947–1951

    CAS  PubMed  Google Scholar 

  42. 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

    Article  PubMed  Google Scholar 

  43. Ozanne A, Krings T, Facon D, Fillard P, Dumas JL, Alvarez H, Ducreux D, Lasjaunias P (2007) MR diffusion tensor imaging and fiber tracking in spinal cord arteriovenous malformations: a preliminary study. AJNR Am J Neuroradiol 28:1271–1279

    Article  CAS  PubMed  Google Scholar 

  44. Rajasekaran S, Kanna RM, Karunanithi R, Shetty AP (2010) Diffusion tensor tractography demonstration of partially injured spinal cord tracts in a patient with posttraumatic brown sequard syndrome. J Magn Reson Imaging 32:978–981

    Article  PubMed  Google Scholar 

  45. Budde MD, Kim JH, Liang HF, Russell JH, Cross AH, Song SK (2008) Axonal injury detected by in vivo diffusion tensor imaging correlates with neurological disability in a mouse model of multiple sclerosis. NMR Biomed 21:589–597

    Article  PubMed Central  PubMed  Google Scholar 

  46. Kim JH, Wu T-H, Budde MD, Lee J-M, Song S-K (2011) Noninvasive detection of brainstem and spinal cord axonal degeneration in an amyotrophic lateral sclerosis mouse model. NMR in Biomedicine 24(2):163–169

    Google Scholar 

  47. Underwood CK, Kurniawan ND, Butler TJ, Cowin GJ, Wallace RH (2010) Non-invasive diffusion tensor imaging detects white matter degeneration in the spinal cord of a mouse model of amyotrophic lateral sclerosis. NeuroImage 1–28

    Google Scholar 

  48. Kim JH, Loy DN, Wang Q, Budde MD, Schmidt RE, Trinkaus K, Song S-K (2010) Diffusion tensor imaging at 3 hours after traumatic spinal cord injury predicts long-term locomotor recovery. J Neurotrauma 27:587–598

    Article  PubMed  Google Scholar 

  49. Loy DN, Kim JH, Xie M, Schmidt RE, Trinkaus K, Song S-K (2007) Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J Neurotrauma 24:979–990

    Article  PubMed  Google Scholar 

  50. Budzik J-F, Balbi V, Thuc V, Duhamel A, Assaker R, Cotten A (2010) Diffusion tensor imaging and fibre tracking in cervical spondylotic myelopathy. Eur Radiol 1–8

    Google Scholar 

  51. Xiangshui M, Xiangjun C, Xiaoming Z, Qingshi Z, Yi C, Chuanqiang Q, Xiangxing M, Chuanfu L, Jinwen H (2010) 3T magnetic resonance diffusion tensor imaging and fibre tracking in cervical myelopathy. Clin Radiol 65:465–473

    Article  CAS  PubMed  Google Scholar 

  52. Shanmuganathan K, Gullapalli RP, Zhuo J, Mirvis SE (2008) Diffusion tensor MR imaging in cervical spine trauma. AJNR Am J Neuroradiol 29:655–659

    Article  CAS  PubMed  Google Scholar 

  53. Chang Y, Jung T-D, Yoo DS, Hyun JK (2010) Diffusion tensor imaging and fiber tractography of patients with cervical spinal cord injury. J Neurotrauma 27:2033–2040

    Article  PubMed  Google Scholar 

  54. Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, de Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130:630–653

    Article  PubMed  Google Scholar 

  55. Tuch DS, Reese TG, Wiegell MR, Wedeen VJ (2003) Diffusion MRI of complex neural architecture. Neuron 40:885–895

    Article  CAS  PubMed  Google Scholar 

  56. Nakamura M, Fujiyosi K et al (2012) Clinical significance of diffusion tensor tractography as a predictor of functional recovery after laminoplasty in patients with cervical compression myelopathy. J Neurosurg Spine 17:147–152

    Article  PubMed  Google Scholar 

  57. Abe O, Takao H, Gonoi W, Sasaki H, Murakami M, Kabasawa H, Kawaguchi H, Goto M, Yamada H, Yamasue H, Kasai K, Aoki S, Ohtomo K (2010) Voxel-based analysis of the diffusion tensor. Neuroradiology 52:699–710

    Article  PubMed  Google Scholar 

  58. Ciccarelli O, Catani M, Johansen-Berg H, Clark C, Thompson A (2008) Diffusion-based tractography in neurological disorders: concepts, applications, and future developments. Lancet Neurol 7:715–727

    Article  PubMed  Google Scholar 

  59. Hikishima K, Quallo MM, Komaki Y, Yamada M, Kawai K, Momoshima S, Okano HJ, Sasaki E, Tamaoki N, Lemon RN, Iriki A, Okano H (2010) Population-averaged standard template brain atlas for the common marmoset (Callithrix jacchus). NeuroImage 1–9

    Google Scholar 

  60. Wright IC, McGuire PK, Poline JB, Travere JM, Murray RM, Frith CD, Frackowiak RS, Friston KJ (1995) A voxel-based method for the statistical analysis of gray and white matter density applied to schizophrenia. NeuroImage 2:244–252

    Article  CAS  PubMed  Google Scholar 

  61. Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE (2006) Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31:1487–1505

    Article  PubMed  Google Scholar 

  62. Smith SM, Jenkinson M, Woolrich MW, Beckmann CF, Behrens TE, 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(suppl 1):S208–219

    Article  PubMed  Google Scholar 

  63. Ashburner J, Friston KJ (2000) Voxel-based morphometry-the methods. NeuroImage 11: 805–821

    Article  CAS  PubMed  Google Scholar 

  64. De Laat KF, Tuladhar AM, Van Norden AGW, Norris DG, Zwiers MP, De Leeuw F-E (2011) Loss of white matter integrity is associated with gait disorders in cerebral small vessel disease. Brain 134:73–83

    Article  PubMed  Google Scholar 

  65. Giuliani N, Calhoun V, Pearlson G, Francis A, Buchanan R (2005) Voxel-based morphometry versus region of interest: a comparison of two methods for analyzing gray matter differences in schizophrenia. Schizophrenia Res 74:135–148

    Article  Google Scholar 

  66. Black KJ, Koller JM, Snyder AZ, Perlmutter JS (2001) Template images for nonhuman primate neuroimaging: 2. Macaque. NeuroImage 14:744–748

    Article  CAS  PubMed  Google Scholar 

  67. Black KJ, Snyder AZ, Koller JM, Gado MH, Perlmutter JS (2001) Template images for nonhuman primate neuroimaging: 1. Baboon. NeuroImage 14:736–743

    Article  CAS  PubMed  Google Scholar 

  68. McLaren DG, Kosmatka KJ, Oakes TR, Kroenke CD, Kohama SG, Matochik JA, Ingram DK, Johnson SC (2009) A population-average MRI-based atlas collection of the rhesus macaque. NeuroImage 45:52–59

    Article  PubMed Central  PubMed  Google Scholar 

  69. Quallo MM, Price CJ, Ueno K, Asamizuya T, Cheng K, Lemon RN, Iriki A (2010) Creating a population-averaged standard brain template for Japanese macaques (M. fuscata). NeuroImage 52:1328–1333

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  70. Rilling JK, Barks SK, Parr LA, Preuss TM, Faber TL, Pagnoni G, Bremner JD, Votaw JR (2007) A comparison of resting-state brain activity in humans and chimpanzees. Proc Natl Acad Sci U S A 104:17146–17151

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  71. Endo T, Spenger C, Westman E, Tominaga T, Olson L (2008) Reorganization of sensory processing below the level of spinal cord injury as revealed by fMRI. Exp Neurol 209:155–160

    Article  PubMed  Google Scholar 

  72. Harel N, Strittmatter S (2008) Functional MRI and other non-invasive imaging technologies: providing visual biomarkers for spinal cord structure and function after injury. Exp Neurol 211:324–328

    Article  PubMed Central  PubMed  Google Scholar 

  73. Lilja J (2006) Blood oxygenation level-dependent visualization of synaptic relay stations of sensory pathways along the neuroaxis in response to graded sensory stimulation of a limb. J Neurosci 26:6330–6336

    Article  CAS  PubMed  Google Scholar 

  74. Tsuji O, Miura K et al (2010) Therapeutic potential of appropriately evaluated ‘safe’ induced pluripotent stem cells for spinal cord injury. Proc Natl Acad Sci U S A 107:12704–12709

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  75. Nori S, Okada S et al (2011) Grafted human-induced pluripotent stem cell–derived neurospheres promote motor functional recovery after spinal cord injury in mice. Proc Natl Acad Sci U S A 108:16825–16830

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  76. Kitamura K et al (2011) Human hepatocyte growth factor promotes functional recovery in primates after spinal cord injury. PLos One 6:e27706

    Article  CAS  PubMed Central  PubMed  Google Scholar 

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Acknowledgements

 This work was supported by grants from MEXT, CREST, JST in Japan, from the General Insurance Association of Japan, from the “Funding Program for World-leading Innovative R&D on Science and Technology,” and by a grant-in-aid from the 21st Century and Global COE Programs of MEXT, Japan, to Keio University.

Animal Preparation: All interventions and animal care procedures were performed in accordance with the Laboratory Animal Welfare Act, Guide for the Care and Use of Laboratory Animals (National Institutes of Health), and Guidelines and Policies for Animal Surgery provided by the Animal Study Committee of the Central Institute for Experimental Animals, and they were approved by the ethics committee of Keio University.

Informed Consent: We obt`ained adequate informed consent from both the healthy volunteer and the patients with cervical spondylotic myelopathy and SCI.

Conflict of Interest: The authors declare that they have no competing financial interests.

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Authors and Affiliations

  1. Department of Orthopaedic Surgery, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan

    Kanehiro Fujiyoshi, Tsunehiko Konomi, Osahiko Tsuji, Yoshiaki Toyama & Masaya Nakamura

  2. Department of Physiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan

    Kanehiro Fujiyoshi, Tsunehiko Konomi, Osahiko Tsuji, Keigo Hikishima & Hideyuki Okano

  3. Department of Orthopedic Surgery, National Hospital Organization, Murayama Medical Center, 2-37-1 Gakuen, Musashimurayama, Tokyo, 208-0011, Japan

    Kanehiro Fujiyoshi & Tsunehiko Konomi

  4. Department of Orthopedic Surgery, Saitama Social Insurance Hospital, 4-9-3 Kitaurawa, Urawa, Saitama-city, Saitama, 330-0074, Japan

    Osahiko Tsuji

  5. Faculty of Radiological Technology, Fujita Health University, School of Health Sciences, 1-98 Dengakugakubo, Kutsukake-cho, Toyoake, Aichi, 470-1192, Japan

    Masayuki Yamada

  6. Central Institute for Experimental Animals, 3-25-12 Tonomachi, Kawasaki, Kanagawa, 210-0821, Japan

    Keigo Hikishima

  7. Department of Radiology, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan

    Suketaka Momoshima

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  1. Kanehiro Fujiyoshi
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Correspondence to Masaya Nakamura .

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Editors and Affiliations

  1. Dept of Orthop & Rehabilit Med, University of Fukui Faculty of Medical Sciences, Yoshida-gun, Fukui, Japan

    Kenzo Uchida

  2. Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Tokyo, Japan

    Masaya Nakamura

  3. Department of Orthopaedic Surgery, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan

    Hiroshi Ozawa

  4. Department of Orthopedic Surgery, Faculty of Medicine The University of Tokushima, Tokushima, Tokushima, Japan

    Shinsuke Katoh

  5. Department of Orthopaedic Surgery, Keio University School of Medicine, Tokyo, Tokyo, Japan

    Yoshiaki Toyama

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Fujiyoshi, K. et al. (2014). Assessment of Injured Spinal Cord Using Diffusion Tensor Tractography. In: Uchida, K., Nakamura, M., Ozawa, H., Katoh, S., Toyama, Y. (eds) Neuroprotection and Regeneration of the Spinal Cord. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54502-6_28

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