Diffusion tensor imaging for trigeminal neuralgia

Diffusion tensor imaging for trigeminal neuralgia

Trigeminal neuralgia (TN) is often classified as type 1 (TN1) when pain is primarily paroxysmal and episodic or type 2 (TN2) when pain is primarily constant in character. Diffusion tensor imaging (DTI) has revealed microstructural changes in the symptomatic trigeminal root and root entry zone of patients with unilateral TN.

Noninvasive DTI analysis of patients with TN may lead to improved trigeminal neuralgia diagnosis of TN subtypes (e.g., TN1 and TN2) and improve patient selection for surgical intervention. DTI measurements may also provide insights into prognosis after intervention, as TN1 patients are known to have better surgical outcomes than TN2 patients 1).


As diffusion tensor imaging (DTI) is able to assess tissue integrity, Leal et al., used diffusion to detect abnormalities in trigeminal nerves (TGN) in patients with trigeminal neuralgia (TN) caused by neurovascular compression (NVC) who had undergone microvascular decompression (MVD).

Using DTI sequencing on a 3-T MRI scanner, we measured the fraction of anisotropy (FA) and apparent diffusion coefficient (ADC) of the TGN in 10 patients who had undergone MVD for TN and in 6 normal subjects. We compared data between affected and unaffected nerves in patients and both nerves in normal subjects (controls). We then correlated these data with CSA and V. Data from the affected side and the unaffected side before and 4 years after MVD were compared.

RESULTS: Before MVD, the FA of the affected side (0.37 ± 0.03) was significantly lower (p < 0.05) compared to the unaffected side in patients (0.48 ± 0.03) and controls (0.52 ± 0.02), and the ADC in the affected side (5.6 ± 0.34 mm2/s) was significantly higher (p < 0.05) compared to the unaffected side in patients (4.26 ± 0.25 mm2/s) and controls (3.84 ± 0.18 mm2/s). Affected nerves had smaller V and CSA compared to unaffected nerves and controls (p < 0.05). After MVD, the FA in the affected side (0.41 ± 0.02) remained significantly lower (p < 0.05) compared to the unaffected side (0.51 ± 0.02), but the ADC in the affected side (4.24 ± 0.34 mm2/s) had become similar (p > 0.05) to the unaffected side (4.01 ± 0.33 mm2/s).

CONCLUSIONS: DTI revealed a loss of anisotropy and an increase in diffusivity in affected nerves before surgery. Diffusion alterations correlated with atrophic changes in patients with TN caused by NVC. After removal of the compression, the loss of FA remained, but ADC normalized in the affected nerves, suggesting improvement in the diffusion of the trigeminal root 2).


Herveh et al. studied the trigeminal nerve in seven healthy volunteers and six patients with trigeminal neuralgia using the diffusion tensor imagingderived parameter fractional anisotropy (FA). While controls did not show a difference between both sides, there was a reduction of FA in the affected nerve in three of six patients with accompanying nerve-vessel conflict and atrophy. Reversibility of abnormally low FA values was demonstrated in one patient successfully treated with microvascular decompression 3).


3T MR diffusion weighted, T1, T2 and FLAIR sequences were acquired for Multiple sclerosis related trigeminal neuralgia MS-TN, TN, and controls. Multi-tensor tractography was used to delineate CN V across cisternal, root entry zone (REZ), pontine and peri-lesional segments. Diffusion metrics including fractional anisotropy (FA), and radial (RD), axial (AD), and mean diffusivities (MD) were measured from each segment.

CN V segments showed distinctive diffusivity patterns. The TN group showed higher FA in the cisternal segment ipsilateral to the side of pain, and lower FA in the ipsilateral REZ segment. The MS-TN group showed lower FA in the ipsilateral peri-lesional segments, suggesting differential microstructural changes along CN V in these conditions.

The study demonstrates objective differences in CN V microstrucuture in TN and MS-TN using non-invasive neuroimaging. This represents a significant improvement in the methods currently available to study pain in MS 4).


The aim of a study was to evaluate the microstructural tissue abnormalities in the trigeminal nerve in symptomatic trigeminal neuralgia not related to neurovascular compression using diffusion tensor imaging. Mean values of the quantitative diffusion parameters of trigeminal nerve, fractional anisotropy and apparent diffusion coefficient, were measured in a group of four symptomatic trigeminal neuralgia patients without neurovascular compression who showed focal non-enhancing T2-hyperintense lesions in the pontine trigeminal pathway. These diffusion parameters were compared between the affected and unaffected sides in the same patient and with four age-matched healthy controls. Cranial magnetic resonance imaging revealed hyperintense lesions in the dorsolateral part of the pons along the central trigeminal pathway on T2-fluid-attenuated inversion recovery sequences. The mean fractional anisotropy value on the affected side was significantly decreased (P = 0.001) compared to the unaffected side and healthy controls. Similarly, the mean apparent diffusion coefficient value was significantly higher (P = 0.001) on the affected side compared to the unaffected side and healthy controls. The cause of trigeminal neuralgia in our patients was abnormal pontine lesions affecting the central trigeminal pathway. The diffusion tensor imaging results suggest that microstructural tissue abnormalities of the trigeminal nerve also exist even in non-neurovascular compression-related trigeminal neuralgia 5).


DTI analysis allows the quantification of structural alterations, even in those patients without any discernible neurovascular contact on MRI. Moreover, our findings support the hypothesis that both the arteries and veins can cause structural alterations that lead to TN. These aspects can be useful for making treatment decisions 6).


The mean diameter of compression arteries (DCA) in NVC patients with TN (1.58 ± 0.34 mm) was larger than that without TN (0.89 ± 0.29 mm). Compared with NVC without TN and HC, the mean values of RD at the site of NVC with TN were significantly increased; however, no significant changes of AD were found between the groups. Correlation analysis showed that DCA positively correlated with radial diffusivity (RD) in NVC patients with and without TN (r = 0.830, p = 0.000). No significant correlation was found between DCA and axial diffusivity (AD) (r = 0.178, p = 0.077).

Larger-diameter compression arteries may increase the chances of TN, and may be a possible facilitating factor for TN 7).


Fractional anisotropy (FA) value quantitatively showed the alteration of trigeminal nerve (TGN) caused by Neurovascular compression (NVC). It provided direct evidence about the effect of NVC which facilitated the diagnosis and surgical decision of Type 2 trigeminal neuralgia (TN) . Besides, significant reduction of FA value may predict an optimistic outcome of microvascular decompression (MVD) 8).


Sophisticated structural MRI techniques including diffusion tensor imaging provide new opportunities to assess the trigeminal nerves and CNS to provide insight into TN etiology and pathogenesis. Specifically, studies have used high-resolution structural MRI methods to visualize patterns of trigeminal nerve-vessel relationships and to detect subtle pathological features at the trigeminal REZ. Structural MRI has also identified CNS abnormalities in cortical and subcortical gray matter and white matter and demonstrated that effective neurosurgical treatment for TN is associated with a reversal of specific nerve and brain abnormalities 9).


Forty-three patients with trigeminal neuralgia were recruited, and diffusion tensor imaging was performed before radiofrequency rhizotomy. By selecting the cisternal segment of the trigeminal nerve manually, they measured the volume of trigeminal nerve, fractional anisotropy, apparent diffusion coefficient, axial diffusivity, and radial diffusivity. The apparent diffusion coefficient and mean value of fractional anisotropy, axial diffusivity, and radial diffusivity were compared between the affected and normal side in the same patient, and were correlated with pre-rhizotomy and post-rhizotomy visual analogue scale pain scores. The results showed the affected side had significantly decreased fractional anisotropy, increased apparent diffusion coefficient and radial diffusivity, and no significant change of axial diffusivity. The volume of the trigeminal nerve on affected side was also significantly smaller. There was a trend of fractional anisotropy reduction and visual analogue scale pain score reduction (P = 0.072). The results suggest that demyelination without axonal injury, and decreased size of the trigeminal nerve, are the microstructural abnormalities of the trigeminal nerve in patients with trigeminal neuralgia caused by neurovascular compression. The application of diffusion tensor imaging in understanding the pathophysiology of trigeminal neuralgia, and predicting the treatment effect has potential and warrants further study 10).

References

1)

Willsey MS, Collins KL, Conrad EC, Chubb HA, Patil PG. Diffusion tensor imaging reveals microstructural differences between subtypes of trigeminal neuralgia. J Neurosurg. 2019 Jul 19:1-7. doi: 10.3171/2019.4.JNS19299. [Epub ahead of print] PubMed PMID: 31323635.
2)

Leal PRL, Roch J, Hermier M, Berthezene Y, Sindou M. Diffusion tensor imaging abnormalities of the trigeminal nerve root in patients with classical trigeminal neuralgia: a pre- and postoperative comparative study 4 years after microvascular decompression. Acta Neurochir (Wien). 2019 May 2. doi: 10.1007/s00701-019-03913-5. [Epub ahead of print] PubMed PMID: 31049710.
3)

Herweh C, Kress B, Rasche D, Tronnier V, Tröger J, Sartor K, Stippich C. Loss of anisotropy in trigeminal neuralgia revealed by diffusion tensor imaging. Neurology. 2007 Mar 6;68(10):776-8. PubMed PMID: 17339587.
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Chen DQ, DeSouza DD, Hayes DJ, Davis KD, O’Connor P, Hodaie M. Diffusivity signatures characterize trigeminal neuralgia associated with multiple sclerosis. Mult Scler. 2016 Jan;22(1):51-63. doi: 10.1177/1352458515579440. PubMed PMID: 25921052.
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Neetu S, Sunil K, Ashish A, Jayantee K, Usha Kant M. Microstructural abnormalities of the trigeminal nerve by diffusion-tensor imaging in trigeminal neuralgia without neurovascular compression. Neuroradiol J. 2016 Feb;29(1):13-8. doi: 10.1177/1971400915620439. PubMed PMID: 26678753; PubMed Central PMCID: PMC4978338.
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Lutz J, Thon N, Stahl R, Lummel N, Tonn JC, Linn J, Mehrkens JH. Microstructural alterations in trigeminal neuralgia determined by diffusion tensor imaging are independent of symptom duration, severity, and type of neurovascular conflict. J Neurosurg. 2016 Mar;124(3):823-30. doi: 10.3171/2015.2.JNS142587. PubMed PMID: 26406792.
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Lin W, Zhu WP, Chen YL, Han GC, Rong Y, Zhou YR, Zhang QW. Large-diameter compression arteries as a possible facilitating factor for trigeminal neuralgia: analysis of axial and radial diffusivity. Acta Neurochir (Wien). 2016 Mar;158(3):521-6. doi: 10.1007/s00701-015-2673-4. PubMed PMID: 26733127; PubMed Central PMCID: PMC4752583.
8)

Chen F, Chen L, Li W, Li L, Xu X, Li W, Le W, Xie W, He H, Li P. Pre-operative declining proportion of fractional anisotropy of trigeminal nerve is correlated with the outcome of micro-vascular decompression surgery. BMC Neurol. 2016 Jul 16;16:106. doi: 10.1186/s12883-016-0620-5. PubMed PMID: 27422267; PubMed Central PMCID: PMC4947245.
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DeSouza DD, Hodaie M, Davis KD. Structural Magnetic Resonance Imaging Can Identify Trigeminal System Abnormalities in Classical Trigeminal Neuralgia. Front Neuroanat. 2016 Oct 19;10:95. Review. PubMed PMID: 27807409; PubMed Central PMCID: PMC5070392.
10)

Chen ST, Yang JT, Yeh MY, Weng HH, Chen CF, Tsai YH. Using Diffusion Tensor Imaging to Evaluate Microstructural Changes and Outcomes after Radiofrequency Rhizotomy of Trigeminal Nerves in Patients with Trigeminal Neuralgia. PLoS One. 2016 Dec 20;11(12):e0167584. doi: 10.1371/journal.pone.0167584. PubMed PMID: 27997548.

Diffusion tensor imaging for brain tumor resection

Diffusion tensor imaging for brain tumor resection

Preserving subcortical connectivity is crucial in optimizing functional outcomes of patients undergoing surgery for intraaxial tumors.

Diffusion tensor imaging (DTI) attempts to aid in the preservation of these subcortical networks by providing a framework for localizing these tracts in relation to the surgical target. DTI takes advantage of the anisotropic diffusion of water along white matter fiber bundles, which can be assessed with magnetic resonance imaging (MRI). Postprocessing platforms are used to map the tracts, which can then be integrated into neuronavigation. This permits the neurosurgeon to ascertain the location and orientation of major white matter tracts for preoperative and intraoperative decision making.


Diffusion tensor imaging (DTI) based on echo planar imaging (EPI) can suffer from geometric image distortions in comparison to conventional anatomical magnetic resonance imaging (MRI). Therefore, DTI-derived information, such as fiber tractography (FT) used for treatment planning of brain tumors, might be associated with spatial inaccuracies when linearly projected on anatomical MRI.

Gerhardt et al., indicated that semi-elastic image fusion can be used for retrospective distortion correction of DTI data acquired for image guidance, such as DTI FT as used for a broad range of clinical indications 1).


The exact utility and practical application of DTI in brain tumor resection continue to be refined. On the one hand, the historical difficulty in obtaining DTI (especially with respect to postprocessing) has made its implementation in neurosurgical practices somewhat limited. Adding to this barrier, the majority of studies describing DTI are placed within a methodological framework that emphasizes the physics and computational analysis of the modality itself, a perspective that is less directly applicable to neurosurgeons wanting to apply DTI to clinical practice. On the other hand, fundamental questions about the utility of the tool have been raised by leaders in the field 2) 3) 4) 5) 6) 7) 8) 9).


Conventional white matter (WM) imaging approaches, such as diffusion tensor imaging (DTI), have been used to preoperatively identify the location of affected WM tracts in patients with intracranial tumors in order to maximize the extent of resection and potentially reduce postoperative morbidity.

Preoperative diffusion tensor imaging (DTI) is used to demonstrate corticospinal tract (CST) position. Intraoperative brain shifts may limit preoperative DTI value, and studies characterizing such shifts are lacking.

For nonenhancing intraaxial tumors, preoperative DTI is a reliable method for assessing intraoperative tumor-to-CST distance because of minimal intraoperative shift, a finding that is important in the interpretation of subcortical motor evoked potential to maximize extent of resection and to preserve motor function. In resection of intra-axial enhancing tumors, intraoperative imaging studies are crucial to compensate for brain shift 10).

Case series

A total of 34 patients were included in this study. Pre-operative contrast-enhanced magnetic resonance imaging and DTI scans of the patients were taken into consideration. Pre- and post-operative neurological examinations were performed and the outcome was assessed.

Preoperative planning of surgical corridor and extent of resection were planned so that maximum possible resection could be achieved without disturbing the WM tracts. DTI indicated the involvement of fiber tracts. A total of 21 (61.7%) patients had a displacement of tracts only and they were not invaded by tumor. A total of 11 (32.3%) patients had an invasion of tracts by the tumor, whereas in 4 (11.7%) patients the tracts were disrupted. Postoperative neurologic examination revealed deterioration of motor power in 4 (11.7%) patients, deterioration of language function in 3 (8.82%) patients, and memory in one patient. Total resection was achieved in 11/18 (61.1%) patients who had displacement of fibers, whereas it was achieved in 5/16 (31.2%) patients when there was infiltration/disruption of tracts.

DTI provided crucial information regarding the infiltration of the tract and their displaced course due to the tumor. This study indicates that it is a very important tool for the preoperative planning of surgery. The involvement of WM tracts is a strong predictor of the surgical outcome 11).

References

1)

Gerhardt J, Sollmann N, Hiepe P, Kirschke JS, Meyer B, Krieg SM, Ringel F. Retrospective distortion correction of diffusion tensor imaging data by semi-elastic image fusion – Evaluation by means of anatomical landmarks. Clin Neurol Neurosurg. 2019 Jun 10;183:105387. doi: 10.1016/j.clineuro.2019.105387. [Epub ahead of print] PubMed PMID: 31228706.
2)

Nimsky C. Fiber tracking: we should move beyond diffusion tensor imaging. World Neurosurg. 2014;82(1-2):35–36.
3)

Farquharson STournier JDCalamante F. et al White matter fiber tractography: why we need to move beyond DTI. J Neurosurg. 2013;118(6):1367–1377.
4)

Fernandez-Miranda JC. Editorial: beyond diffusion tensor imaging. J Neurosurg. 2013;118(6):1363–1365; discussion 1365-1366.
5)

Lerner AMogensen MAKim PEShiroishi MSHwang DHLaw M. Clinical applications of diffusion tensor imaging. World Neurosurg. 2014;82(1-2):96–109.
6)

Feigl GCHiergeist WFellner C. et al Magnetic resonance imaging diffusion tensor tractography: evaluation of anatomic accuracy of different fiber tracking software packages. World Neurosurg. 2014;81(1):144–150.
7)

Duffau H. The dangers of magnetic resonance imaging diffusion tensor tractography in brain surgery. World Neurosurg. 2014;81(1):56–58.
8)

Duffau H. Diffusion tensor imaging is a research and educational tool, but not yet a clinical tool. World Neurosurg. 2014;82(1-2):e43–e45.
9)

Potgieser ARWagemakers Mvan Hulzen ALde Jong BMHoving EWGroen RJ. The role of diffusion tensor imaging in brain tumor surgery: a review of the literature. Clin Neurol Neurosurg. 2014;124C:51–58.
10)

Shahar T, Rozovski U, Marko NF, Tummala S, Ziu M, Weinberg JS, Rao G, Kumar VA, Sawaya R, Prabhu SS. Preoperative Imaging to Predict Intraoperative Changes in Tumor-to-Corticospinal Tract Distance: An Analysis of 45 Cases Using High-Field Intraoperative Magnetic Resonance Imaging. Neurosurgery. 2014 Jul;75(1):23-30. doi: 10.1227/NEU.0000000000000338. PubMed PMID: 24618800.
11)

Dubey A, Kataria R, Sinha VD. Role of Diffusion Tensor Imaging in Brain Tumor Surgery. Asian J Neurosurg. 2018 Apr-Jun;13(2):302-306. doi: 10.4103/ajns.AJNS_226_16. PubMed PMID: 29682025; PubMed Central PMCID: PMC5898096.

Diffusion tensor imaging for degenerative cervical myelopathy

Diffusion tensor imaging for degenerative cervical myelopathy

Despite its invasiveness, computed tomography myelography (CTM) is still considered an important supplement to conventional magnetic resonance imaging (MRI) for preoperative evaluation of multilevel degenerative cervical myelopathy. Schöller et al., analyzed if diffusion tensor imaging (DTI) could be a less invasive alternative for this purpose.

In 20 patients with degenerative cervical myelopathy and an indication for decompression of at least one level, CTM was performed preoperatively to determine the extent of spinal canal/cerebrospinal fluid (CSF) space and cord compression (Naganawa score) for a decision on the number of levels to be decompressed. Fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were correlated with these parameters and with MRI-based increased signal intensity (ISI). Receiver operating characteristic analysis was performed to determine the sensitivity to discriminate levels requiring decompression surgery. European Myelopathy Score(EMS) and neck/radicular visual analog scale (VAS-N/R) were used for clinical evaluation.

According to preoperative CTM, 20 levels of maximum and 16 levels of relevant additional stenosis were defined and decompressed. Preoperative FA and particularly ADC showed a significant correlation with the CTM Naganawa score but also with the ISI grade. Furthermore, both FA and ADC facilitated a good discrimination between stenotic and nonstenotic levels with cutoff values < 0.49 for FA and > 1.15 × 10-9 m2/s for ADC. FA and especially ADC revealed a considerably higher sensitivity (79% and 82%, respectively) in discriminating levels requiring decompression surgery compared with ISI (55%). EMS and VAS-N/R were significantly improved at 14 months compared with preoperative values.

DTI parameters are highly sensitive at distinguishing surgical from nonsurgical levels in CSM patients and might therefore represent a less invasive alternative to CTM for surgical planning 1).


A study population included 50 patients with symptoms of cervical myelopathy. The patients were evaluated based on symptoms using the European myelopathy scoring system and were divided into: Grade 1, including patients with mild symptoms; Grade 2, referring to patients with moderate symptoms and Grade 3, which included patients revealing severe symptoms. All the patients were investigated with a 1.5 T MRI unit acquiring DWI and DTI sequences. FA and ADC values from each spinal segment were analyzed in terms of Frequency, Percentage, Mean, Standard Deviation and Confidence Intervals. The comparison of values was done by ANOVA and post hoc analysis by bonferroni test. Comparison of accuracy of FA, ADC and T2WI in recognizing myelopathic changes was done by t-test. Receiver Operating Characteristics (ROC) analysis was performed to obtain a cut off value of FA and ADC for each spinal level to identify myelopathic change in the spinal cord.

The study revealed a significant difference in the mean FA and ADC value of stenotic and Non-stenotic segments. T2WI was highly significant (p = 0.000) in recognizing myelopathy changes in patients falling under Grade 2(moderate) and Grade 3(severe) according to European Myelopathy scoring system. Regarding patients under Grade 1 (mild) FA and ADC values showed significant difference compared to T2WI. The collective sensitivity in the identification of myelopathic changes was highest with FA (79%) as compared to ADC (71%) and T2WI (50%). ROC analysis was done to determine the cut off values of FA and ADC at each cervical spine segments. The proposed cut off, for FA and ADC at the level of C1-C2 is 0.68 and 0.92, C2-C3 is 0.65 and 1.03, C3-C4 is 0.63 and 1.01, C4-C5 0.61 and 0.98, At C5-C6 0.57 and 1.04, At C6-C7 0.56 and 0.96 respectively.

FA and ADC values enhance the efficacy and accuracy of MRI in the diagnosis of cervical spondylotic myelopathy. Hence diffusion tensor imaging can be used as a non-invasive modality to recognize spondylotic myelopathy changes even in the early stages, which can be helpful in deciding on appropriate timing of decompression surgery before the irreversible chronic changes set in 2).


A meta-analysis was conducted to assess alterations in measures of diffusion tensor imaging (DTI) in the patients of cervical spondylotic myelopathy (CSM), exploring the potential role of DTI as a diagnosis biomarker. A systematic search of all related studies written in English was conducted using PubMed, Web of Science, EMBASE, CINAHL, and Cochrane comparing CSM patients with healthy controls. Key details for each study regarding participants, imaging techniques, and results were extracted. DTI measurements, such as fractional anisotropy (FA), apparent diffusion coefficient (ADC), and mean diffusivity (MD) were pooled to calculate the effect size (ES) by fixed or random effects meta-analysis. 14 studies involving 479 CSM patients and 278 controls were identified. Meta-analysis of the most compressed levels (MCL) of CSM patients demonstrated that FA was significantly reduced (ES -1.52, 95% CI -1.87 to -1.16, P < 0.001) and ADC was significantly increased (ES 1.09, 95% CI 0.89 to 1.28, P < 0.001). In addition, a notable ES was found for lowered FA at C2-C3 for CSM vs. controls (ES -0.83, 95% CI -1.09 to -0.570, P < 0.001). Meta-regression analysis revealed that male ratio of CSM patients had a significant effect on reduction of FA at MCL (P = 0.03). The meta-analysis of DTI studies of CSM patients clearly demonstrated a significant FA reduction and ADC increase compared with healthy subjects. This result supports the use of DTI parameters in differentiating CSM patients from health subjects. Future researches are required to investigate the diagnosis performance of DTI in cervical spondylotic myelopathy 3).


The measurement of DTI indexes within the spinal cord provides a quantitative assessment of neural damage in various spinal cord pathologies. DTI studies in animal models of spinal cord injury indicate that DTI is a reliable imaging technique with important histological and functional correlates.

DTI is a noninvasive marker of microstructural change within the spinal cord. In human studies, spinal cord DTI shows definite changes in subjects with acute and chronic spinal cord injury, as well as cervical spondylotic myelopathy. Interestingly, changes in DTI indexes are visualized in regions of the cord, which appear normal on conventional magnetic resonance imaging and are remote from the site of cord compression. Spinal cord DTI provides data that can help us understand underlying microstructural changes within the cord and assist in prognostication and planning of therapies 4).

References

1)

Schöller K, Siller S, Brem C, Lutz J, Zausinger S. Diffusion Tensor Imaging for Surgical Planning in Patients with Cervical Spondylotic Myelopathy. J Neurol Surg A Cent Eur Neurosurg. 2019 Jun 10. doi: 10.1055/s-0039-1691822. [Epub ahead of print] PubMed PMID: 31181580.
2)

Nukala M, Abraham J, Khandige G, Shetty BK, Rao APA. Efficacy of diffusion tensor imaging in identification of degenerative cervical spondylotic myelopathy. Eur J Radiol Open. 2018 Dec 12;6:16-23. doi: 10.1016/j.ejro.2018.08.006. eCollection 2019. PubMed PMID: 30581892; PubMed Central PMCID: PMC6293016.
3)

Guan X, Fan G, Wu X, Gu G, Gu X, Zhang H, He S. Diffusion tensor imaging studies of cervical spondylotic myelopathy: a systemic review and meta-analysis. PLoS One. 2015 Feb 11;10(2):e0117707. doi: 10.1371/journal.pone.0117707. eCollection 2015. Review. PubMed PMID: 25671624; PubMed Central PMCID: PMC4363894.
4)

Vedantam A, Jirjis MB, Schmit BD, Wang MC, Ulmer JL, Kurpad SN. Diffusion tensor imaging of the spinal cord: insights from animal and human studies. Neurosurgery. 2014 Jan;74(1):1-8. doi: 10.1227/NEU.0000000000000171. PubMed PMID: 24064483.
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