From the Department of Radiology, Northwell Lenox Hill Hospital, 100 E 77th St, New York, NY 10075 (R.S.); Zwanger-Pesiri Radiology, Lindenhurst, New York, NY (D.S.); Department of Radiology, Metro North Hospital and Health Service, Brisbane, Australia (J.G.); and Department of Neuroradiology, QScan Radiology, Queensland, Australia (J.G.).
Radiographics. 2024 Oct;44(10):e240023. doi: 10.1148/rg.240023.
Of the twelve cranial nerves, nine supply motor innervation to the muscles of the head and neck. Loss of this motor nerve supply, or denervation, follows a series of predictable chronologic changes in the affected muscles. Although the length of time between each change is markedly variable, denervation is typically classified into three distinct time points: acute, subacute, and chronic. These muscle changes produce characteristic findings on images, with contrast-enhanced MRI being the preferred modality for assessment. Imaging allows radiologists to not only identify denervation but also evaluate the extent of denervation and localize the potential site of insult. However, these findings may be easily mistaken for other diseases with similar manifestations, such as neoplasm, infection, and inflammatory conditions. As such, it is fundamental for radiologists to be familiar with cranial nerve anatomy and denervation patterns so that they can avoid these potential pitfalls and focus their imaging search on the pathway of the affected nerve. In this article, the anatomy and muscles innervated by motor cranial nerves in the head and neck, denervation, and the associated expected imaging patterns are reviewed, and examples of potential pitfalls and denervation mimics are provided. RSNA, 2024.
在 12 对颅神经中，有 9 对支配头颈部的肌肉运动。运动神经供应丧失或去神经支配后，受影响的肌肉会发生一系列可预测的时间顺序变化。尽管每个变化之间的时间长度明显不同，但去神经支配通常可分为三个不同的时间点：急性、亚急性和慢性。这些肌肉变化在图像上产生特征性表现，增强磁共振成像（MRI）是评估的首选方式。成像不仅可以让放射科医生识别去神经支配，还可以评估去神经支配的程度并定位潜在的损伤部位。然而，这些表现可能很容易与具有相似表现的其他疾病（如肿瘤、感染和炎症性疾病）相混淆。因此，放射科医生必须熟悉颅神经解剖结构和去神经支配模式，以避免这些潜在的陷阱，并将影像学搜索重点放在受影响神经的通路。本文回顾了头颈部运动颅神经的解剖结构和支配的肌肉、去神经支配以及相关的预期成像模式，并提供了潜在陷阱和去神经模拟的示例。RSNA，2024 年。
