扩散张量磁共振成像在慢性脊髓压迫中的应用
Summary
在这里, 我们提出了一个协议, 应用扩散张量成像参数来评估脊髓受压。
Abstract
慢性脊髓压迫是非创伤性脊髓损伤患者脊髓损伤的最常见原因。传统磁共振成像 (MRI) 在确认诊断和评估压缩程度方面发挥着重要作用。然而, 传统 MRI 提供的解剖细节不足以准确估计神经元损伤和/或评估慢性脊髓压迫患者神经元恢复的可能性。相反, 扩散张量成像 (DTI) 可以根据水分子在组织中扩散的检测提供定量结果。在本研究中, 我们开发了一个方法框架, 以说明 DTI 在慢性脊髓压迫疾病中的应用。DTI 分数各向异性 (FA)、表观扩散系数 (Adc) 和特征向量值可用于可视化脊髓的微观结构病理变化。与健康对照组相比, 慢性脊髓压迫患者的 FA 降低, Adc 和特征向量值增加。DTI 可以帮助外科医生了解脊髓损伤的严重程度, 并提供有关预后和神经功能恢复的重要信息。总之, 该协议提供了一个敏感的, 详细的, 非侵入性的工具来评估脊髓压迫。
Introduction
慢性脊髓压迫是脊髓损伤最常见的原因1。这种情况可能是由于后纵向韧带骨化, 血肿, 颈椎间盘突出, 椎体变性, 或椎管内肿瘤 2,3。慢性脊髓压迫可导致不同程度的功能缺陷;然而, 有严重脊髓压迫的临床病例, 没有任何神经症状和体征, 以及轻度脊髓压迫但严重神经缺陷的患者 4。在这种情况下, 敏感成像对于评估压缩严重程度和确定损伤范围至关重要。
传统的 MRI 在阐明脊髓解剖学中起着重要作用。这种技术通常被用来评估压缩程度, 因为它对软组织的敏感性5。许多参数可以通过 MRI 测量, 如 MR 信号强度、脊髓形态和椎管区域。然而, MRI 有一些局限性, 只提供定性信息, 而不是定量结果6。慢性脊髓压迫患者的 MRI 强度通常有异常信号变化。然而, 临床症状和 MRI 强度变化之间的差异使得很难诊断一个功能状况完全基于 MRI 特征7。以往的研究强调了这一争议, 即脊髓8中 MRI T2 超强的预后值。两组报告 T2 脊髓超强是慢性脊髓压迫术后预后不良的参数 8, 9。相反, 一些作者发现 t2 信号变化与预后8,9之间没有明显的联系。Chen 等人和 Vedantam 等人将 MRI T2 超强分为两类, 与不同的预后 10、11 相对应。1型表现为模糊、模糊、模糊的边界, 这一类别表现为可逆组织学变化。2型图像呈现强烈的、明确定义的边框, 对应于不可逆的病理损害。传统的 ttent2 MRI 技术没有提供足够的信息来识别这两类患者和评估患者的预后。相比之下, DTI, 一种更复杂的成像技术, 可以通过通过水分子扩散定量检测组织中的微观结构变化, 帮助获得更具体的预后信息。
近年来, DTI 由于能够描述脊髓微结构而受到越来越多的关注。DTI 可以测量水分子在组织中扩散的方向和大小。DTI 参数可以定量评估慢性脊髓压迫患者的神经损伤。FA 和 ADC 是脊髓评估中最常用的参数。Fa 值揭示了周围轴突纤维定向的各向异性程度, 并描述了解剖边界12,13。Adc 值提供了关于三维空间中分子运动在许多方向上的特征的信息, 并揭示了沿三个主轴6、12 的扩散系数。这些参数的变化与影响水分子扩散的微观结构变化有关。因此, 外科医生可以利用 DTI 参数来识别脊髓病理。本研究提供了 DTI 方法和过程, 为治疗慢性脊髓压迫患者提供了更详细的预后信息。
Protocol
Representative Results
Discussion
常规 MRI 通常用于评估各种脊柱疾病患者的预后。然而, 这种成像方式提供宏观解剖细节, 而不是微观结构评估14, 这限制了神经功能的预测。此外, 传统的 MRI 可能低估了脊髓损伤的严重程度和程度。DTI 的出现可以通过提供有关水分子扩散的定量信息, 帮助外科医生更准确地评估脊髓功能。
在本研究中, 描述了一个方法框架, 以证明 DTI 参数在慢性脊髓压迫患者…
Declarações
The authors have nothing to disclose.
Acknowledgements
本研究得到了中国广州科技项目 (201607010021 号) 和江西自然科学基金 (20142BAB205065 号) 的支持。
Materials
| 3-Tesla MRI scanner | Siemens | 40708 | Software: NUMARIS/4 |
| Syngo MR B17 | Siemens | 40708 | Software: NUMARIS/4 |
Referências
- Sun, G. D., et al. A progressive compression model of thoracic spinal cord injury in mice: function assessment and pathological changes in spinal cord. Neural Regeneration Research. 12 (8), 1365-1374 (2017).
- Watanabe, N., et al. Neurological Recovery after Posterior Spinal Surgery in Patients with Metastatic Epidural Spinal Cord Compression. Acta Medica Okayama. 70 (6), 449 (2016).
- Tatsui, C. E., et al. Spinal Laser Interstitial Thermal Therapy: A Novel Alternative to Surgery for Metastatic Epidural Spinal Cord Compression. Neurosurgery. 79 Suppl 1 (suppl_1), S73 (2016).
- Zheng, W., et al. Application of Diffusion Tensor Imaging Cutoff Value to Evaluate the Severity and Postoperative Neurologic Recovery of Cervical Spondylotic Myelopathy. World Neurosurgery. 118, e849-e855 (2018).
- Ellingson, B. M., Salamon, N., Holly, L. T. Imaging techniques in spinal cord injury. World Neurosurgery. 82 (6), 1351-1358 (2014).
- Zhao, C., et al. Diffusion tensor imaging of spinal cord parenchyma lesion in rat with chronic spinal cord injury. Magnetic Resonance Imaging. 47, 25-32 (2018).
- Mohanty, C., Massicotte, E. M., Fehlings, M. G., Shamji, M. F. The Association of Preoperative Cervical Spine Alignment with Spinal Cord Magnetic Resonance Imaging Hyperintensity and Myelopathy Severity: Analysis of a Series of 124 Cases. Spine. 40 (1), 11-16 (2015).
- Tetreault, L. A., et al. Systematic review of magnetic resonance imaging characteristics that affect treatment decision making and predict clinical outcome in patients with cervical spondylotic myelopathy. Spine. 38 (22 Suppl 1), S89 (2013).
- Nouri, A. . The Role of Magnetic Resonance Imaging in Predicting Surgical Outcome in Patients with Degenerative Cervical Myelopathy. , (2015).
- Chen, C. J., Lyu, R. K., Lee, S. T., Wong, Y. C., Wang, L. J. Intramedullary high signal intensity on T2-weighted MR images in cervical spondylotic myelopathy: prediction of prognosis with type of intensity. Radiology. 221 (3), 789-794 (2001).
- Vedantam, A., Jonathan, A., Rajshekhar, V. Association of magnetic resonance imaging signal changes and outcome prediction after surgery for cervical spondylotic myelopathy. Journal of Neurosurgery Spine. 15 (6), 660 (2011).
- Vedantam, A., et al. Diffusion tensor imaging of the spinal cord: insights from animal and human studies. Neurosurgery. 74 (1), 1-8 (2014).
- Bazley, F. A., et al. DTI for assessing axonal integrity after contusive spinal cord injury and transplantation of oligodendrocyte progenitor cells. Conference Proceedings: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2012 (4), 82-85 (2012).
- Lewis, M., Yap, P. T., Mccullough, S., Olby, N. The relationship between lesion severity characterized by diffusion tensor imaging and motor function in chronic canine spinal cord injury. Journal of Neurotrauma. 35 (3), (2018).
- Hagmann, P., et al. Understanding diffusion MR imaging techniques: from scalar diffusion-weighted imaging to diffusion tensor imaging and beyond. Radiographics. 26 Suppl 1 (suppl_1), S205 (2006).
- Zheng, W., et al. Time course of diffusion tensor imaging metrics in the chronic spinal cord compression rat model. Acta Radiologica. , 284185118795335 (2018).
- Jones, J. G., Cen, S. Y., Lebel, R. M., Hsieh, P. C., Law, M. Diffusion Tensor Imaging Correlates with the Clinical Assessment of Disease Severity in Cervical Spondylotic Myelopathy and Predicts Outcome following Surgery. American Journal of Neuroradiology. 34 (2), 471-478 (2013).
- Kerkovský, M., et al. Magnetic resonance diffusion tensor imaging in patients with cervical spondylotic spinal cord compression: correlations between clinical and electrophysiological findings. Spine. 37 (1), 48-56 (2012).
- Zheng, W., et al. Application of Diffusion Tensor Imaging Cutoff Value to Evaluate the Severity and Postoperative Neurologic Recovery of Cervical Spondylotic Myelopathy. World Neurosurgery. 118, e849-e855 (2018).
- Thurnher, M. M., Law, M. Diffusion-weighted imaging, diffusion-tensor imaging, and fiber tractography of the spinal cord. Magnetic Resonance Imaging Clinics of North America. 17 (2), 225-244 (2009).
- Cadotte, A., et al. Spinal Cord Segmentation by One Dimensional Normalized Template Matching: A Novel, Quantitative Technique to Analyze Advanced Magnetic Resonance Imaging Data. PLOS ONE. 10 (10), e0139323 (2015).
