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特邀文章:基于磁共振成像的白质疾病诊断方法

Invited article: an MRI-based approach to the diagnosis of white matter disorders.

作者信息

Schiffmann Raphael, van der Knaap Marjo S

机构信息

Institute of Metabolic Disease, Baylor Research Institute, Dallas, TX, USA.

出版信息

Neurology. 2009 Feb 24;72(8):750-9. doi: 10.1212/01.wnl.0000343049.00540.c8.

DOI:10.1212/01.wnl.0000343049.00540.c8
PMID:19237705
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC2677542/
Abstract

BACKGROUND

There are many different white matter disorders, both inherited and acquired, and consequently the diagnostic process is difficult. Establishing a specific diagnosis is often delayed at great emotional and financial costs. The pattern of brain structures involved, as visualized by MRI, has proven to often have a high diagnostic specificity.

METHODS

We developed a comprehensive practical algorithm that relies mainly on the characteristics of brain MRI.

RESULTS

The initial decision point defines a hypomyelination pattern, in which the cerebral white matter is hyperintense (normal), isointense, or slightly hypointense relative to the cortex on T1-weighted images, vs other pathologies with more prominent hypointensity of the cerebral white matter on T1-weighted images. In all types of pathology, the affected white matter is hyperintense on T2-weighted images, but, as a rule, the T2 hyperintensity is less marked in hypomyelination than in other pathologies. Some hypomyelinating disorders are typically associated with peripheral nerve involvement, while others are not. Lesions in patients with pathologies other than hypomyelination can be either confluent or isolated and multifocal. Among the diseases with confluent lesions, the distribution of the abnormalities is of high diagnostic value. Additional MRI features, such as white matter rarefaction, the presence of cysts, contrast enhancement, and the presence of calcifications, further narrow the diagnostic possibilities.

CONCLUSION

Application of a systematic decision tree in MRI of white matter disorders facilitates the diagnosis of specific etiologic entities.

摘要

背景

存在许多不同的白质疾病,包括遗传性和后天性的,因此诊断过程具有挑战性。建立特定诊断往往会延迟,造成巨大的情感和经济代价。通过MRI可视化的受累脑结构模式已被证明通常具有较高的诊断特异性。

方法

我们开发了一种主要依赖于脑MRI特征的综合实用算法。

结果

初始决策点定义了一种髓鞘形成不足模式,在T1加权图像上,相对于皮质,脑白质呈高信号(正常)、等信号或轻度低信号,而在T1加权图像上脑白质低信号更明显的其他病变则不同。在所有类型的病变中,受累白质在T2加权图像上呈高信号,但通常情况下,髓鞘形成不足时T2高信号不如其他病变明显。一些髓鞘形成不足性疾病通常伴有周围神经受累,而其他疾病则不然。除髓鞘形成不足外的其他病变患者的病变可融合或孤立且多灶性。在病变融合的疾病中,异常分布具有很高的诊断价值。其他MRI特征,如白质稀疏、囊肿的存在、对比增强和钙化的存在,进一步缩小了诊断可能性。

结论

在白质疾病的MRI中应用系统决策树有助于诊断特定的病因实体。

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