Diffusion tensor imaging (DTI) is a magnetic resonance (MR) technique capable of measuring the magnitude and direction of diffusion of water molecules in various tissues. DTI developed from a technique known as diffusion-weighted imaging, which measures the attenuation of MR signals caused by diffusion, and was initially used for brain imaging.1 DTI was formally introduced by Basser et al,2 and subsequent improvements in this technique have led to the development of DTI as a tool to delineate white matter tracts in the brain.
DTI of the spinal cord in humans was initially inadequate because of the small area of the cord, susceptibility artifacts, and cardiac and respiratory motion artifacts.3,4 Improvements in scanning protocols have allowed usable diffusion images of the spinal cord. Spinal cord DTI, initially performed in animals, is now used to evaluate spinal cord disorders in humans. Investigators have shown that DTI is able to detect cord damage in regions of the cord that appear normal on T2-weighted images.5,6 Spinal cord DTI therefore represents an important advancement in the field of neuroimaging, and its use is being expanded both for prognostication and for guiding therapy.
In this article, we review the literature on spinal cord DTI in both animal models and humans. We provide a summary for the clinical use of spinal cord DTI in a few neurosurgical conditions. We hope that by providing a review of the current status of spinal cord DTI, we may be able to better direct future efforts in this field.
PRINCIPLES OF DTI
Diffusion MR imaging (MRI) provides a measure of the displacement of water molecules in tissues. Displaced water molecules produce an attenuated signal during diffusion MR scanning. By its nature, the axonal architecture in the white matter of the central nervous system promotes diffusion of water molecules in a direction predominantly parallel, rather than perpendicular, to axon fibers.2,7,8 Diffusion perpendicular to the fibers seems to be limited by cell membranes more than myelin sheaths.9,10 This direction-dependent diffusion, described as anisotropy, is used by DTI to infer the orientation of surrounding axonal fibers and to delineate anatomic boundaries. DTI uses a tensor framework to characterize molecular motion in multiple directions in a 3-dimensional space. The diffusivities along the 3 principal axes are used to calculate DTI indexes. The commonly used indexes for spinal cord DTI include fractional anisotropy (FA), apparent diffusion coefficient (ADC), longitudinal apparent diffusion co-efficient (lADC), and transverse ADC. Investigators determine specific regions of interest on axial or sagittal diffusion images, and DTI indexes for these regions are calculated from individual vectors using dedicated software tools. FA, which ranges from 0 to 1, defines the degree of anisotropy, and tissues with high anisotropy such as white matter tracts have a value closer to 1. Injured spinal cords show a decrease in anisotropy resulting from disruption of longitudinally aligned axons and exhibit a decrease in FA. The ADC or mean diffusivity is the mathematical average of the diffusivities in the 3 principal axes, and its value may increase or decrease depending on the histopathological progression of the lesion. The lADC represents rostrocaudal diffusivity along white matter fibers and is often decreased in the presence of axonal injury.11 Transverse ADC measures radial diffusivity and is characteristically increased in the presence of demyelination.11,12 Overall, DTI indexes are affected by microstructural alterations that affect the diffusion of water molecules, and this forms the basis for using DTI indexes to identify spinal cord pathology.
DTI STUDIES IN RAT MODELS
DTI Measurements of Rat Spinal Cord
DTI measurements of the rat spinal cord were initially performed either ex vivo13-15 or in vivo with implantable coils.16,17 The majority of these studies used scanners with field strengths from 4.7 to 7 T. With improved technology, in vivo measurements were possible with higher-field-strength scanners18,19 and without implantable coils.18,19 Studies with animal spinal cords indicate that DTI values clearly differentiate white and gray matter (Figure 1).13,17,18,20 Because diffusion occurs preferentially along axonal bundles, white matter is significantly more anisotropic than gray matter.10,20 Significant differences in DTI indexes are described between spinal levels (cervical, thoracic, and caudal) in rat studies.18 This is probably a result of microstructural variations in the gray and white matter along the spinal cord.21 These results indicate that diffusion properties are not uniform throughout the length of the cord and vary according to the level being studied. These results further establish the usefulness of DTI to delineate neural structures in the spinal cord.
DTI Measurements After Spinal Cord Injury
One of the important applications of DTI is the evaluation of spinal cord injury (SCI) in animal models. DTI demonstrates a significant decrease in anisotropy and increase in radial diffusivity at the level of injury16,22,23 and in areas of the cord that are apparently normal on conventional T2-weighted images.24 In hyperacute SCI (0-6 hours), diffusion measurements are able to distinguish SCI on the basis of severity.25 However, the unique feature of DTI is its ability to detect changes in diffusion metrics at regions rostral and caudal to the lesion.16,26-28 A decrease in diffusivity remote from the lesion is observed during recovery from SCI (Figure 2).27 These findings are possibly related to cytotoxic edema, axonal loss, or chronic atrophy.29-31 Interestingly, changes in DTI indexes away from the lesion correlate with injury severity, indicating that they may be used as surrogate markers of neural injury (Figure 2). Moreover, these changes are not limited to the white matter tracts only. At our center, we find that motor neurons rostral to the lesion are enlarged after SCI and that this is associated with an increase in the FA of the rostral gray matter (unpublished data). Studies show that spinal cord gray matter is affected by ischemia as a result of impaired microvascular perfusion32 and is characterized by astrogliosis during recovery.33 Using DTI to track these remote changes will help us better understand the pathophysiology of SCI. Because there are changes in diffusivities throughout the cord after SCI, it is apparent that microstructural recovery from SCI is not limited to the epicenter alone.
Several animal studies show correlations between DTI indexes and histological changes during recovery from SCI.25,34-37 The hyperacute phase after SCI is associated with edema, hemorrhage, and inflammation. Following this, there is an intermediate phase characterized by a robust glial response and revascularization process. The chronic phase of SCI shows wallerian degeneration, astroglial scar formation, and progressive cavitation of the cord with rostral-caudal spreading.34,38 Identifying specific changes in DTI metrics to characterize particular histological events during recovery from SCI remains a challenge. Although an increase in mean diffusivity after injury can map the extent of degeneration, a decrease in FA is sensitive to cavity formation within the cord.34 DTI is also able to characterize the orientation of the glial scar and the degree of axonal dieback and preservation.14,15 Changes in DTI measurements possibly reflect a combination of histopathological changes.28,39,40 DTI values have been shown to be more affected by axonal injury than demyelination,28,40 suggesting that the diverse tissue damage as a result of SCI may not be completely captured by diffusion measurements.
DTI and Functional Correlates in SCI
DTI metrics correlate with electrophysiological measures, indicating that specific diffusion measures could be used as predictors of neurological function. The use of cortical sensory evoked potentials to assess cord integrity in SCI models has been limited by its sensitivity to anesthetic agents41,42 and changes in body temperature.43 Spinal sensory evoked potentials represent a reliable technique to obtain repeated recordings,44,45 and these correlate well with the Basso, Beattie, and Bresnahan score.46 DTI measurements of the medial spinothalamic tracts and dorsal columns correlate with very early and early components of the spinal sensory evoked potentials, whereas diffusion measures of the lateral spinothalamic tracts are linked to the late components (Figure 3).47 Other studies show that the lADC of the rostral white matter correlates with the Basso, Beattie, and Bresnahan score,16 and the radial diffusivity caudal to the lesion correlates with the grid walk test.28 The lADC of the spared ventrolateral white matter can also predict hind-limb motor recovery using the Basso mouse scale.48 Because axonal structure and integrity are closely linked to MR diffusion measurements,21,23 the above correlations emphasize the utility of DTI to measure both the structural and functional properties of axons.
The role of DTI in therapeutic interventions for SCI is the focus of a few animal studies. The radial diffusivity around the injured site correlates with behavioral recovery in rats that are transplanted with fibroblasts after SCI.26 At our center, we find significantly increased diffusivity rostral to the injury site in rat SCI models after stem cell transplantation compared with rats that received placebo. In the future, it is expected that spinal cord DTI will be used to monitor transplantations and other therapeutic interventions for SCI.
DTI STUDIES IN HUMANS
DTI in the Intact Human Spinal Cord
Spinal cord DTI studies in healthy human subjects show feasibility and reliability of this procedure.49-54 Good contrast is observed between gray and white regions, with the highly anisotropic white matter showing much higher FA values than the central gray matter (Figure 4). Although the magnitude of FA of the whole cord decreases in the rostral-caudal direction, the mean diffusivity is relatively constant throughout the cord. DTI indexes are age dependent and reflect microstructural changes in the spinal cord associated with aging.55-59 These results show that DTI is sensitive to degenerative changes within the spinal cord that are not visualized on conventional MRI. Moreover, they emphasize the need to compare DTI measurements in patients with age-matched control subjects.
DTI in Human SCI
In acute human SCI, DTI shows a reduction in diffusivity, particularly FA and lADC, around the injury site.60,61 Choosing a DTI parameter that best characterizes SCI remains a challenge, and authors suggest that diffusivity along the individual axes is more useful than DTI indexes in representing microstructural changes.13 Similar to animal studies, human SCI is characterized by changes in diffusivity rostral to the injury site, in regions of the cord that appear normal on conventional MRI,61,62 and possibly reflect retrograde neural injury. Axial FA maps and tractography are also sensitive to asymmetric cord damage in acute SCI and can supplement conventional MRI in this setting.63,64
The prognostic value of DTI indexes in acute SCI is still unclear. Higher ADC values at the injured site are shown to be associated with better postoperative Neurosurgical Cervical Spine Scale scores but not Frankel Scale measures.65 Another report shows that the DTI indexes are correlated with the American Spinal Injury Association motor score in patients with nonhemorrhagic contusions.62 Correlations between DTI parameters and other outcome scales such as the Functional Independence Measure, Walking Index for Spinal Cord Injury, and Spinal Cord Injury Measure have not been explored. There is a need to use a standardized functional outcome score to define the prognostic value of DTI indexes. Moreover, if diffusivities of individual white matter tracts within the spinal cord are measured, it becomes essential to correlate the diffusion indexes to scales that measure sensory and motor function separately.
Chronic SCI is associated with a number of microstructural neural changes, including demyelination,66,67 remyelination,68,69 axonal loss,68 and atrophy,70 that affect the diffusion of water molecules. As opposed to acute SCI, the injury site is characterized by increased diffusivity in patients with chronic SCI. FA at the injury site, however, is greatly reduced and appears to depend on both the level of injury and the completeness of the injury.71 FA values and connection rates of fiber tracking have also been shown to correlate with motor score in patients with chronic cervical cord injury.72 Similar to acute SCI, diffusivity within the high cervical spinal cord, rostral to the chronic injury site, is significantly altered.71,73,74 Importantly, rostral DTI indexes correlate with functional measures in this group of patients,73,74 thereby demonstrating that these indexes may be noninvasive imaging biomarkers for SCI. Additionally, spinal cord DTI indexes rostral to the injury site correlate with DTI indexes within cranial white matter tracts and could be used as a marker of neural reorganization and plasticity.74 Because spinal fixation hardware around the injury site creates artifacts on diffusion images, DTI of the spinal cord, rostral to the injury site, allows us to evaluate neural injury without directly imaging the injury site. This may be a useful approach for future studies that investigate longitudinal changes in diffusivity during recovery from SCI.
DTI Applications in Cervical Spondylotic Myelopathy
The complex pathophysiology includes mechanical spinal cord compression caused by disk protrusion, osteophytes, or ossified posterior longitudinal ligament and secondary cord ischemia.75,76 Histopathological changes within the cervical cord in cervical spondylotic myelopathy include cavitation, demyelination, and regions of cord infarction.77 Diffusion MRI is able to detect cord changes in patients with narrow cervical canals despite normal T1- and T2-weighted images.5,49,78-80 Across studies, FA is shown to be lower at the affected level in patients compared with corresponding levels in control subjects. DTI indexes in cervical spondylotic myelopathy patients appear to depend on the degree of cord damage. Symptomatic cervical spondylotic myelopathy patients have lower FA values and higher ADC measures at the compressed level compared with asymptomatic patients with radiological features of cord compression.81 However, DTI measurements do not have consistent correlations with clinical scores of patients with cervical spondylotic myelopathy.80,82-84 It therefore appears that DTI has a role to play in the preoperative planning of cervical spondylotic myelopathy patients, but the use of DTI to decide on surgical intervention or monitor recovery has yet to be investigated in detail.
DTI for Spinal Cord Tumors
Diffusion tensor tractography is currently used to describe the orientation and location of white matter fibers around brain tumors.85-87 Recent studies have used tractography for intradural spinal cord tumors.88,89 Fiber tracking to delineate displaced white matter tracts seems to be particularly useful in solid tumors. In cystic tumors and tumors with considerable vasogenic edema, the increased diffusion of water molecular can lead to erroneous fiber tracking. A recent study showed that diffusion tensor tractography has a sensitivity of 87.5% and a specificity of 100% for predicting tumor resectability preoperatively.90 Measurement of diffusion indexes within spinal cord tumors suggests that higher tumor mass is characterized by a decrease in FA and an increase in ADC. However, studies have yet to evaluate the utility of DTI indexes as predictors of tumor histology. In this regard, DTI indexes may be able to differentiate spinal cord lesions on conventional MRIs and provide surgeons with an idea about the possible pathology. Overall, the use of DTI shows much promise in planning surgical approaches for spinal cord tumors, as it has in brain tumor resection.
DTI has been used in a variety of other spinal cord disorders, including multiple sclerosis,91,92 syringomyelia,93,94 and transverse myelitis.95 Although many of these studies are able to characterize DTI parameters in diseased states, the routine use of spinal cord DTI in the clinical setting is yet to be realized.
Limitations of DTI
Spinal cord DTI in humans still has a number of limitations. Adequate spatial resolution remains a problem, and it is difficult to visualize the individual funiculi on diffusion-weighted images, particularly in the lower thoracic cord.54 DTI of these segments is affected more by artifacts arising from cardiac and respiratory motion and cerebrospinal fluid pulsation.96 The use of faster imaging techniques such as parallel imaging and single-shot echo-planar imaging and the use of cardiac pulse gating have helped to reduce these artifacts. However, scan acquisition time is still a limitation for patients with acute SCI because these patients often cannot withstand additional scanning time in the MRI suite. In addition, the signal-to-noise ratio is not uniform throughout the cervical spinal cord and is significantly decreased in caudal segments.59,97 A low signal-to-noise ratio can lead to overestimation of anisotropy measures, particularly in low-anisotropy tissues such as the central gray matter.98 The use of 3-T MR scanners improves the signal-to-noise ratio99 but they are still not used universally. The use of DTI postoperatively is hampered significantly by the use of spinal instrumentation, which creates numerous artifacts. Additionally, standardized software to process tensor images is essential to make this a feasible option for routine clinical use.
DTI provides unique insight into the pathophysiology and microstructural alterations associated with spinal cord disorders. Although initial studies in rat models have primed this modality for human research, more data are required on the accuracy and reliability of DTI indexes in defining cord pathology. DTI of the spinal cord shows promise in certain neurosurgical conditions such as traumatic SCI, cervical spondylotic myelopathy, and spinal cord tumors. However, scanning protocols and image processing need to be refined and standardized. Once these challenges are overcome, we can expect the use of DTI in mainstream clinical practice both to prognosticate and to monitor patients with spinal cord disease.
Funding was provided by VA Rehab R&D grant 1 I01 RX000113-01 and the Bryon Riesch Paralysis Foundation Endowment. The authors have no personal financial or institutional interest in any of the drugs, materials, or devices described in this article.
1. Le Bihan D, Breton E, Lallemand D, Grenier P, Cabanis E, Laval-Jeantet M. MR imaging of intravoxel incoherent motions: application to diffusion and perfusion in neurologic disorders. Radiology. 1986;161(2):401–407.
2. Basser PJ, Mattiello J, LeBihan D. MR diffusion tensor spectroscopy and imaging. Biophys J. 1994;66(1):259–267.
3. Clark CA, Werring DJ. Diffusion tensor imaging in spinal cord: methods and applications: a review. NMR Biomed. 2002;15(7-8):578–586.
4. Barker GJ. Diffusion-weighted imaging of the spinal cord and optic nerve. J Neurol Sci. 2001;186(suppl 1):S45–S49.
5. Demir A, Ries M, Moonen CT, et al.. Diffusion-weighted MR imaging with apparent diffusion coefficient and apparent diffusion tensor maps in cervical spondylotic myelopathy. Radiology. 2003;229(1):37–43.
6. Shen H, Tang Y, Huang L, et al.. Applications of diffusion-weighted MRI in thoracic spinal cord injury without radiographic abnormality. Int Orthop. 2007;31(3):375–383.
7. Basser PJ, Pierpaoli C. Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. J Magn Reson B. 1996;111(3):209–219.
8. Basser PJ. Inferring microstructural features and the physiological state of tissues from diffusion-weighted images. NMR Biomed. 1995;8(7-8):333–344.
9. Beaulieu C, Allen PS. Determinants of anisotropic water diffusion in nerves. Magn Reson Med. 1994;31(4):394–400.
10. Beaulieu C. The basis of anisotropic water diffusion in the nervous system: a technical review. NMR Biomed. 2002;15(7-8):435–455.
11. Song SK, Sun SW, Ju WK, Lin SJ, Cross AH, Neufeld AH. Diffusion tensor imaging detects and differentiates axon and myelin degeneration in mouse optic nerve after retinal ischemia. Neuroimage. 2003;20(3):1714–1722.
12. Song SK, Sun SW, Ramsbottom MJ, Chang C, Russell J, Cross AH. Dysmyelination revealed through MRI as increased radial (but unchanged axial) diffusion of water. Neuroimage. 2002;17(3):1429–1436.
13. Schwartz ED, Hackney DB. Diffusion-weighted MRI and the evaluation of spinal cord axonal integrity following injury and treatment. Exp Neurol. 2003;184(2):570–589.
14. Schwartz ED, Duda J, Shumsky JS, Cooper ET, Gee J. Spinal cord diffusion tensor imaging and fiber tracking can identify white matter tract disruption and glial scar orientation following lateral funiculotomy. J Neurotrauma. 2005;22(12):1388–1398.
15. Schwartz ED, Chin CL, Shumsky JS, et al.. Apparent diffusion coefficients in spinal cord transplants and surrounding white matter correlate with degree of axonal dieback after injury in rats. AJNR Am J Neuroradiol. 2005;26(1):7–18.
16. Deo AA, Grill RJ, Hasan KM, Narayana PA. In vivo serial diffusion tensor imaging of experimental spinal cord injury. J Neurosci Res. 2006;83(5):801–810.
17. Madi S, Hasan KM, Narayana PA. Diffusion tensor imaging of in vivo and excised rat spinal cord at 7 T with an icosahedral encoding scheme. Magn Reson Med. 2005;53(1):118–125.
18. Ellingson BM, Kurpad SN, Li SJ, Schmit BD. In vivo diffusion tensor imaging of the rat spinal cord at 9.4T. J Magn Reson Imaging. 2008;27(3):634–642.
19. Bilgen M, Al-Hafez B, Berman NE, Festoff BW. Magnetic resonance imaging of mouse spinal cord. Magn Reson Med. 2005;54(5):1226–1231.
20. Moseley ME, Cohen Y, Kucharczyk J, et al.. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology. 1990;176(2):439–445.
21. Schwartz ED, Cooper ET, Fan Y, et al.. MRI diffusion coefficients in spinal cord correlate with axon morphometry. Neuroreport. 2005;16(1):73–76.
22. Shi R, Pryor JD. Pathological changes of isolated spinal cord axons in response to mechanical stretch. Neuroscience. 2002;110(4):765–777.
23. Ford JC, Hackney DB, Lavi E, Phillips M, Patel U. Dependence of apparent diffusion coefficients on axonal spacing, membrane permeability, and diffusion time in spinal cord white matter. J Magn Reson Imaging. 1998;8(4):775–782.
24. Ford JC, Hackney DB, Alsop DC, et al.. MRI characterization of diffusion coefficients in a rat spinal cord injury model. Magn Reson Med. 1994;31(5):488–494.
25. Loy DN, Kim JH, Xie M, Schmidt RE, Trinkaus K, Song SK. Diffusion tensor imaging predicts hyperacute spinal cord injury severity. J Neurotrauma. 2007;24(6):979–990.
26. Schwartz ED, Shumsky JS, Wehrli S, Tessler A, Murray M, Hackney DB. Ex vivo MR determined apparent diffusion coefficients correlate with motor recovery mediated by intraspinal transplants of fibroblasts genetically modified to express BDNF. Exp Neurol. 2003;182(1):49–63.
27. Ellingson BM, Kurpad SN, Schmit BD. Ex vivo diffusion tensor imaging and quantitative tractography of the rat spinal cord during long-term recovery from moderate spinal contusion. J Magn Reson Imaging. 2008;28(5):1068–1079.
28. Sundberg LM, Herrera JJ, Narayana PA. In vivo longitudinal MRI and behavioral studies in experimental spinal cord injury. J Neurotrauma. 2010;27(10):1753–1767.
29. Loubinoux I, Volk A, Borredon J, et al.. Spreading of vasogenic edema and cytotoxic edema assessed by quantitative diffusion and T2 magnetic resonance imaging. Stroke. 1997;28(2):419–426.
30. Lu H, Sun SQ. A correlative study between AQP4 expression and the manifestation of DWI after the acute ischemic brain edema in rats. Chin Med J (Engl). 2003;116(7):1063–1069.
31. Ellingson BM, Ulmer JL, Prost RW, Schmit BD. Morphology and morphometry in chronic spinal cord injury assessed using diffusion tensor imaging and fuzzy logic. Conf Proc IEEE Eng Med Biol Soc. 2006;1:1885–1888.
32. Koyanagi I, Tator CH, Theriault E. Silicone rubber microangiography of acute spinal cord injury in the rat. Neurosurgery. 1993;32(2):260–268.
33. Barrett CP, Guth L, Donati EJ, Krikorian JG. Astroglial reaction in the gray matter lumbar segments after midthoracic transection of the adult rat spinal cord. Exp Neurol. 1981;73(2):365–377.
34. Ellingson BM, Schmit BD, Kurpad SN. Lesion growth and degeneration patterns measured using diffusion tensor 9.4-T magnetic resonance imaging in rat spinal cord injury. J Neurosurg Spine. 2010;13(2):181–192.
35. Zhang J, Jones M, DeBoy CA, et al.. Diffusion tensor magnetic resonance imaging of wallerian degeneration in rat spinal cord after dorsal root axotomy. J Neurosci. 2009;29(10):3160–3171.
36. Kozlowski P, Raj D, Liu J, Lam C, Yung AC, Tetzlaff W. Characterizing white matter damage in rat spinal cord with quantitative MRI and histology. J Neurotrauma. 2008;25(6):653–676.
37. Farrell JA, Zhang J, Jones MV, et al.. q-Space and conventional diffusion imaging of axon and myelin damage in the rat spinal cord after axotomy. Magn Reson Med. 2010;63(5):1323–1335.
38. Norenberg MD, Smith J, Marcillo A. The pathology of human spinal cord injury: defining the problems. J Neurotrauma. 2004;21(4):429–440.
39. Herrera JJ, Chacko T, Narayana PA. Histological correlation of diffusion tensor imaging metrics in experimental spinal cord injury. J Neurosci Res. 2008;86(2):443–447.
40. Budde MD, Kim JH, Liang HF, et al.. Toward accurate diagnosis of white matter pathology using diffusion tensor imaging. Magn Reson Med. 2007;57(4):688–695.
41. Mongan PD, Peterson RE. Intravenous anesthetic alterations on the spinal-sciatic evoked response in swine. Anesth Analg. 1993;77(1):149–154.
42. Rojas MJ, Navas JA, Rector DM. Evoked response potential markers for anesthetic and behavioral states. Am J Physiol Regul Integr Comp Physiol. 2006;291(1):R189–R196.
43. Oro J, Haghighi SS. Effects of altering core body temperature on somatosensory and motor evoked potentials in rats. Spine (Phila Pa 1976). 1992;17(5):498–503.
44. Nordwall A, Axelgaard J, Harada Y, Valencia P, McNeal DR, Brown JC. Spinal cord monitoring using evoked potentials recorded from feline vertebral bone. Spine (Phila Pa 1976). 1979;4(6):486–494.
45. Lueders H, Gurd A, Hahn J, Andrish J, Weiker G, Klem G. A new technique for intraoperative monitoring of spinal cord function: multichannel recording of spinal cord and subcortical evoked potentials. Spine (Phila Pa 1976). 1982;7(2):110–115.
46. Ellingson BM, Kurpad SN, Schmit BD. Characteristics of mid- to long-latency spinal somatosensory evoked potentials following spinal trauma in the rat. J Neurotrauma. 2008;25(11):1323–1334.
47. Ellingson BM, Kurpad SN, Schmit BD. Functional correlates of diffusion tensor imaging in spinal cord injury. Biomed Sci Instrum. 2008;44:28–33.
48. Kim JH, Loy DN, Wang Q, et al.. Diffusion tensor imaging at 3 hours after traumatic spinal cord injury predicts long-term locomotor recovery. J Neurotrauma. 2010;27(3):587–598.
49. Ries M, Jones RA, Dousset V, Moonen CT. Diffusion tensor MRI of the spinal cord. Magn Reson Med. 2000;44(6):884–892.
50. Clark CA, Barker GJ, Tofts PS. Magnetic resonance diffusion imaging of the human cervical spinal cord in vivo. Magn Reson Med. 1999;41(6):1269–1273.
51. Holder CA, Muthupillai R, Mukundan S Jr, Eastwood JD, Hudgins PA. Diffusion-weighted MR imaging of the normal human spinal cord in vivo. AJNR Am J Neuroradiol. 2000;21(10):1799–1806.
52. Bammer R, Fazekas F, Augustin M, et al.. Diffusion-weighted MR imaging of the spinal cord. AJNR Am J Neuroradiol. 2000;21(3):587–591.
53. Nagayoshi K, Kimura S, Ochi M, et al.. Diffusion-weighted echo planar imaging of the normal human cervical spinal cord. J Comput Assist Tomogr. 2000;24(3):482–485.
54. Ellingson BM, Ulmer JL, Kurpad SN, Schmit BD. Diffusion tensor MR imaging of the neurologically intact human spinal cord. AJNR Am J Neuroradiol. 2008;29(7):1279–1284.
55. Agosta F, Lagana M, Valsasina P, et al.. Evidence for cervical cord tissue disorganisation with aging by diffusion tensor MRI. Neuroimage. 2007;36(3):728–735.
56. Mamata H, Jolesz FA, Maier SE. Apparent diffusion coefficient and fractional anisotropy in spinal cord: age and cervical spondylosis-related changes. J Magn Reson Imaging. 2005;22(1):38–43.
57. Lindberg PG, Feydy A, Maier MA. White matter organization in cervical spinal cord relates differently to age and control of grip force in healthy subjects. J Neurosci. 2010;30(11):4102–4109.
58. Van Hecke W, Leemans A, Sijbers J, Vandervliet E, Van Goethem J, Parizel PM. A tracking-based diffusion tensor imaging segmentation method for the detection of diffusion-related changes of the cervical spinal cord with aging. J Magn Reson Imaging. 2008;27(5):978–991.
59. Vedantam A, Jirjis MB, Schmit BD, Wang MC, Ulmer JL, Kurpad SN. Characterization and limitations of diffusion tensor imaging metrics in the cervical spinal cord in neurologically intact subjects. [published online ahead of print Feb 8 2013]. J Magn Reson Imaging. 2013;38(4):861–867.
60. Facon D, Ozanne A, Fillard P, Lepeintre JF, Tournoux-Facon C, Ducreux D. MR diffusion tensor imaging and fiber tracking in spinal cord compression. AJNR Am J Neuroradiol. 2005;26(6):1587–1594.
61. Shanmuganathan K, Gullapalli RP, Zhuo J, Mirvis SE. Diffusion tensor MR imaging in cervical spine trauma. AJNR Am J Neuroradiol. 2008;29(4):655–659.
62. Cheran S, Shanmuganathan K, Zhuo J, et al.. Correlation of MR diffusion tensor imaging parameters with ASIA motor scores in hemorrhagic and nonhemorrhagic acute spinal cord injury. J Neurotrauma. 2011;28(9):1881–1892.
63. Vedantam A, Jirjis MB, Schmit BD, et al.. Diffusion tensor imaging and tractography in Brown-Sequard syndrome. Spinal Cord. 2012;50(12):928–930.
64. Rajasekaran S, Kanna RM, Karunanithi R, Shetty AP. Diffusion tensor tractography demonstration of partially injured spinal cord tracts in a patient with posttraumatic Brown Sequard syndrome. J Magn Reson Imaging. 2010;32(4):978–981.
65. Endo T, Suzuki S, Utsunomiya A, Uenohara H, Tominaga T. Prediction of neurological recovery using apparent diffusion coefficient in cases of incomplete spinal cord injury. Neurosurgery. 2011;68(2):329–336.
66. Totoiu MO, Keirstead HS. Spinal cord injury is accompanied by chronic progressive demyelination. J Comp Neurol. 2005;486(4):373–383.
67. Bunge RP, Puckett WR, Becerra JL, Marcillo A, Quencer RM. Observations on the pathology of human spinal cord injury: a review and classification of 22 new cases with details from a case of chronic cord compression with extensive focal demyelination. Adv Neurol. 1993;59:75–89.
68. Blight AR, Decrescito V. Morphometric analysis of experimental spinal cord injury in the cat: the relation of injury intensity to survival of myelinated axons. Neuroscience. 1986;19(1):321–341.
69. Harrison BM, McDonald WI. Remyelination after transient experimental compression of the spinal cord. Ann Neurol. 1977;1(6):542–551.
70. Potter K, Saifuddin A. Pictorial review: MRI of chronic spinal cord injury. Br J Radiol. 2003;76(905):347–352.
71. Ellingson BM, Ulmer JL, Kurpad SN, Schmit BD. Diffusion tensor MR imaging in chronic spinal cord injury. AJNR Am J Neuroradiol. 2008;29(10):1976–1982.
72. Chang Y, Jung TD, Yoo DS, Hyun JK. Diffusion tensor imaging and fiber tractography of patients with cervical spinal cord injury. J Neurotrauma. 2010;27(11):2033–2040.
73. Petersen JA, Wilm BJ, von Meyenburg J, et al.. Chronic cervical spinal cord injury: DTI correlates with clinical and electrophysiological measures. J Neurotrauma. 2012;29(8):1556–1566.
74. Freund P, Schneider T, Nagy Z, et al.. Degeneration of the injured cervical cord is associated with remote changes in corticospinal tract integrity and upper limb impairment. PLoS One. 2012;7(12):e51729.
75. Baptiste DC, Fehlings MG. Pathophysiology of cervical myelopathy. Spine J. 2006;6(suppl 6):190S–197S.
76. Baron EM, Young WF. Cervical spondylotic myelopathy: a brief review of its pathophysiology, clinical course, and diagnosis. Neurosurgery. 2007;60(1 supp1 1):S35–S41.
77. Ono K, Ota H, Tada K, Yamamoto T. Cervical myelopathy secondary to multiple spondylotic protrusions: a clinicopathologic study. Spine. 1977;2(2):109–125.
78. Song T, Chen WJ, Yang B, et al.. Diffusion tensor imaging in the cervical spinal cord. Eur Spine J. 2011;20(3):422–428.
79. Kara B, Celik A, Karadereler S, et al.. The role of DTI in early detection of cervical spondylotic myelopathy: a preliminary study with 3-T MRI. Neuroradiology. 2011;53(8):609–616.
80. Budzik JF, Balbi V, Le Thuc V, Duhamel A, Assaker R, Cotten A. Diffusion tensor imaging and fibre tracking in cervical spondylotic myelopathy. Eur Radiol. 2011;21(2):426–433.
81. Kerkovsky M, Bednarik J, Dusek L, et al.. Magnetic resonance diffusion tensor imaging in patients with cervical spondylotic spinal cord compression: correlations between clinical and electrophysiological findings. Spine. 2012;37(1):48–56.
82. Lee JW, Kim JH, Park JB, et al.. Diffusion tensor imaging and fiber tractography in cervical compressive myelopathy: preliminary results. Skeletal Radiol. 2011;40(12):1543–1551.
83. Jones JG, Cen SY, Lebel RM, Hsieh PC, Law M. Diffusion tensor imaging correlates with the clinical assessment of disease severity in cervical spondylotic myelopathy and predicts outcome following surgery. AJNR Am J Neuroradiol. 2012;34(2):471–478.
84. Uda T, Takami T, Tsuyuguchi N, et al.. Assessment of cervical spondylotic myelopathy using diffusion tensor MRI parameter at 3.0 Tesla. Spine (Phila Pa 1976). 2012;38(5):407–414.
85. Wieshmann UC, Symms MR, Parker GJ, et al.. Diffusion tensor imaging demonstrates deviation of fibres in normal appearing white matter adjacent to a brain tumour. J Neurol Neurosurg Psychiatry. 2000;68(4):501–503.
86. Price SJ, Burnet NG, Donovan T, et al.. Diffusion tensor imaging of brain tumours at 3T: a potential tool for assessing white matter tract invasion? Clin Radiol. 2003;58(6):455–462.
87. Witwer BP, Moftakhar R, Hasan KM, et al.. Diffusion-tensor imaging of white matter tracts in patients with cerebral neoplasm. J Neurosurg. 2002;97(3):568–575.
88. Vargas MI, Delavelle J, Jlassi H, et al.. Clinical applications of diffusion tensor tractography of the spinal cord. Neuroradiology. 2008;50(1):25–29.
89. Ducreux D, Lepeintre JF, Fillard P, Loureiro C, Tadie M, Lasjaunias P. MR diffusion tensor imaging and fiber tracking in 5 spinal cord astrocytomas. AJNR Am J Neuroradiol. 2006;27(1):214–216.
90. Setzer M, Murtagh RD, Murtagh FR, et al.. Diffusion tensor imaging tractography in patients with intramedullary tumors: comparison with intraoperative findings and value for prediction of tumor resectability. J Neurosurg Spine. 2010;13(3):371–380.
91. Werring DJ, Clark CA, Barker GJ, Thompson AJ, Miller DH. Diffusion tensor imaging of lesions and normal-appearing white matter in multiple sclerosis. Neurology. 1999;52(8):1626–1632.
92. van Hecke W, Nagels G, Emonds G, et al.. A diffusion tensor imaging group study of the spinal cord in multiple sclerosis patients with and without T2 spinal cord lesions. J Magn Reson Imaging. 2009;30(1):25–34.
93. Roser F, Ebner FH, Maier G, Tatagiba M, Nägele T, Klose U. Fractional anisotropy levels derived from diffusion tensor imaging in cervical syringomyelia. Neurosurgery. 2010;67(4):901–905.
94. Hatem SM, Attal N, Ducreux D, et al.. Assessment of spinal somatosensory systems with diffusion tensor imaging in syringomyelia. J Neurol Neurosurg Psychiatry. 2009;80(12):1350–1356.
95. Renoux J, Facon D, Fillard P, Huynh I, Lasjaunias P, Ducreux D. MR diffusion tensor imaging and fiber tracking in inflammatory diseases of the spinal cord. AJNR Am J Neuroradiol. 2006;27(9):1947–1951.
96. Thurnher MM, Law M. Diffusion-weighted imaging, diffusion-tensor imaging, and fiber tractography of the spinal cord. Magn Reson Imaging Clin N Am. 2009;17(2):225–244.
97. Wheeler-Kingshott CA, Hickman SJ, Parker GJ, et al.. Investigating cervical spinal cord structure using axial diffusion tensor imaging. Neuroimage. 2002;16(1):93–102.
98. Bastin ME, Armitage PA, Marshall I. A theoretical study of the effect of experimental noise on the measurement of anisotropy in diffusion imaging. Magn Reson Imaging. 1998;16(7):773–785.
99. Carballido-Gamio J, Xu D, Newitt D, Han ET, Vigneron DB, Majumdar S. Single-shot fast spin-echo diffusion tensor imaging of the lumbar spine at 1.5 and 3 T. Magn Reson Imaging. 2007;25(5):665–670.
In the article, we are able to see how an initially complex technique has evolved into an essential clinical tool. Indeed, the concept of diffusion-weighted imaging is far from new, but because of an inherent susceptibility to motion artifacts, it was at first almost impossible to use in the brain. However, this was overcome with the use of fast so-called echo-planar techniques that froze motion, allowing a new contrast from the brain tissue that represented molecular motion. One was thus able to measure units called the apparent diffusion coefficient and the fractional anisotropy of tissues. This was a breakthrough for cerebral ischemia but also led to the development of even fancier imaging techniques such as tractography. Although these techniques have been standard for brain imaging for quite some time, at the level of the spinal cord, it was considered for a very long time almost impossible to perform these techniques, at least in the living (there were some initial beautiful experiments with isolated cat spinal cords), and as for most modern neuroimaging techniques, spinal imaging was considered the poor cousin. Indeed, at the level of the spine, one has motion, pulsation, and susceptibility artifacts galore, and let us not forget that the areas we want to image are a fraction of that of the brain. However, with further advances in both software and hardware in magnetic resonance techniques, great progress has been made so that now these diffusion-derived techniques can be performed at the level of the spinal cord. This represents potentially an even greater breakthrough because one is able to see intramedullary changes caused by displacement of the fibers resulting from either intrinsic or extrinsic processes with a degree of contrast that was unbelievable just half a decade ago. This should allow not just the detection of the presence of intramedullary lesions but also the determination of their effect on neighboring white matter structures to preserve them during treatment, which should allow a more precise diagnosis to improve outcomes of patients with spinal diseases. One may also soon be able to use the sensitivity to motion to differentiate between different pathological entities, as one currently does in brain imaging when using diffusion-weighted imaging for brain masses.
Karl Olof Lövblad
1. Diffusion tensor imaging relies on measuring differences in the directional diffusion of water molecules in tissues. What is this direction-dependent diffusion called?
A. Apparent diffusion coefficient
C. Axial diffusivity
D. Mean diffusivity
E. Radial diffusivity
2. What are the predicted changes in fractional anisotropy (FA) and radial diffusivity (RD) following acute spinal cord injury?
A. FA increased, RD increased
B. FA increased, RD decreased
C. FA decreased, RD increased
D. FA decreased, RD decreased
E. FA unchanged, RD decreased
3. A patient with an acute thoracic spinal cord injury undergoes decompression and instrumented stabilization. What is the best imaging modality for following the evolution of the cord injury?
A. CT myelogram
B. DTI MRI at the site of injury
C. DTI MRI rostral to the injury
D. MRI with gadolinium
E. Functional MRI of the injured site