前脑无裂
医学
胼胝体发育不全
导水管狭窄
产前诊断
解剖
脑积水
第四脑室
孔脑
侧脑室
心室系统
脉络丛
大脑镰
胎儿
胼胝体
放射科
脑积水
病理
中枢神经系统
怀孕
生物
内分泌学
遗传学
作者
Sarah W. Cater,Brita K. Boyd,Peter R. Eby
出处
期刊:Radiographics
[Radiological Society of North America]
日期:2020-09-01
卷期号:40 (5): 1458-1472
被引量:21
标识
DOI:10.1148/rg.2020200034
摘要
Fetal central nervous system (CNS) abnormalities are second only to cardiac malformations in their frequency of occurrence. Early and accurate diagnosis at prenatal US is therefore essential, allowing improved prenatal counseling and facilitating appropriate referral. Thorough knowledge of normal intracranial anatomy and adoption of a logical sonographic approach can improve depiction of abnormal findings, leading to a more accurate differential diagnosis earlier in pregnancy. Four standard recommended views—transventricular, falx, cavum, and posterior fossa or transcerebellar views—provide an overview of fetal intracranial anatomy during the second trimester anatomy scan. Essential elements surveyed in the head and neck include the lateral cerebral ventricles, choroid plexus, midline falx, cavum septi pellucidi, cerebellum, cisterna magna, upper lip, and nuchal fold. CNS abnormalities can be organized into six main categories at prenatal US. Developmental anomalies include neural tube defects and neuronal migration disorders. Posterior fossa disorders include Dandy-Walker malformation variants and Chiari II malformation. Ventricular anomalies include aqueductal stenosis. Midline disorders include those on the spectrum of holoprosencephaly, agenesis of the corpus callosum, and septo-optic dysplasia. Vascular anomalies include vein of Galen malformations. Miscellaneous disorders include hydranencephaly, porencephaly, tumors, and intracranial hemorrhage. Correlation with postnatal MRI is helpful for confirmation and clarification of suspected diagnoses after birth. The authors discuss a standard US imaging approach to the fetal CNS and review cases in all categories of CNS malformations, providing postnatal MRI correlation when available. ©RSNA, 2020
科研通智能强力驱动
Strongly Powered by AbleSci AI