PREDICTIVE VALUE OF DIFFUSION MRI IN CERVICAL SPONDYLOTIC MYELOPATHY

项目摘要 退行性脊髓型颈椎病是脊髓损伤最常见的原因 这是一个严重的公共卫生问题。一个主要的缺点限制了改善治疗的努力 CSM患者的最大问题是缺乏可量化的指标来作为临床决策的基础。Advanced MRI 扩散张量成像（DTI）等技术在这一领域显示出了希望。DTI测量 组织中水位移的大小、各向异性和方向性，并提供了 沿着白色物质束的定向扩散率。虽然DTI提供了一个有价值的工具来评估白色物质 完整性，不幸的是，我们发现目前的DTI技术是有缺陷的，因为扩散特性推导 随着病理解剖复杂性的增加，DTI失去了特异性和敏感性。因此 使用DTI预测长期结果仍不确定。为了克服干扰DTI分析的因素， 我们开发了弥散基础频谱成像（DBSI），以更准确地描绘白色物质损伤， 允许在环境中区分和定量轴突损伤/损失、脱髓鞘和炎症 脊髓压迫的症状除了轴突/髓鞘损伤之外，DBSI还量化水肿/组织损失， 改进的成像生物标志物，其更准确地预测患者的临床过程、对治疗的反应，以及 长期预后。该提案的长期目标是建立和验证非侵入性成像 作为临床病程和对手术减压的治疗反应的预测因子的生物标志物， CSM患者。第一个目标将评估脊髓DBSI病理指标是否反映神经系统 脊柱减压手术后神经功能损伤及远期预后预测 关于CSM本研究旨在验证轻度脊髓型颈椎病的脊髓压迫临床表现与脊髓损伤的关系。 主要是水肿和炎症的反映，真正的轴突丢失的发生率较低; 相反，在中度CSM患者中观察到的功能恢复的高变异性可归因于 脊髓压迫导致永久性轴突缺失的风险更大。这项建议的第二个目的， 将完善DBSI模型，以评估血流不足对脊髓病理学的影响，并改善 CSM中轴突损失定量的准确性。这一目的将检验脊髓的作用 可以通过在DBSI中包括体素内不相干运动（IVIM）来评估CSM病理的血流 DBSI导出的轴突体积的准确性可以通过包括轴突内扩散来提高 DBSI建模中的组件。 这种非侵入性DBSI生物标志物的鉴定和验证将为临床应用提供指导。 管理，长期预后和家庭咨询。非侵入性生物标志物的验证 在脊髓型颈椎病的手术治疗中预测功能恢复将代表一种新的， 脊髓型颈椎病治疗的实质性进展。

Project Abstract Degenerative cervical spondylotic myelopathy (CSM) is the most common cause of spinal cord injury (SCI) representing a significant public health problem. A major shortcoming limiting efforts to improve the treatment of patients with CSM is the lack of quantifiable metrics on which to base clinical decisions. Advanced MRI techniques, such as diffusion tensor imaging (DTI) have shown promise in this area. DTI measures the magnitude, anisotropy, and directionality of water displacement in tissue and provides quantifiable measures of directional diffusivity along white matter tracts. While DTI provides a valuable tool to assess white matter integrity, unfortunately, we have found current DTI techniques are flawed because diffusion properties derived using DTI lose specificity and sensitivity with increasing pathological and anatomical complexity. Thus the prediction of long-term outcome using DTI remains uncertain. To overcome factors confounding DTI analysis, we developed diffusion basis spectrum imaging (DBSI), to more accurately delineate white matter injury, allowing differentiation and quantification of axonal injury/loss, demyelination, and inflammation in the setting of spinal cord compression. DBSI quantifies edema/tissue loss in addition to axon/myelin injury, providing improved imaging biomarkers that more accurately predict a patient's clinical course, response to therapy, and long-term prognosis. The long-term objective of this proposal is to establish and validate non-invasive imaging biomarkers that are predictors of clinical course and therapeutic response to surgical decompression in patients with CSM. The first aim will assess whether spinal cord DBSI pathological metrics reflect neurological impairments and predict long-term neurologic outcomes following decompressive spinal surgery in patients with CSM. This aim will test the hypothesis that clinical manifestations of spinal cord compression in mild CSM are predominantly a reflection of edema and inflammation, with a lower incidence of true axonal loss; In contrast the high variability of functional recovery observed in moderate CSM patients is attributable to a greater risk for permanent axonal loss caused by spinal cord compression. The second aim of this proposal, will refine DBSI modeling for assessing effects of blood flow deficits on spinal cord pathology and improving the accuracy of axonal loss quantification in CSM. This aim will test the hypothesis that the effect of spinal cord blood flow on CSM pathology may be assessed by including Intra-Voxel-Incoherent-Motion (IVIM) in DBSI modeling; the accuracy of DBSI-derived axon volume may be improved by including intra-axonal diffusion component in DBSI modeling. The identification and validation of such non-invasive DBSI biomarkers will provide guidance on clinical management, long-term prognosis, and family counseling. The validation of a non-invasive biomarker for predicting functional recovery in the surgical management of cervical myelopathy would represent a new and substantial advance in the treatment of cervical myelopathy.