Im K, Guimaraes A, Kim Y, Cottrill E, Gagoski B, Rollins C, Ortinau C, Yang E, Grant P E
From the Fetal Neonatal Neuroimaging and Developmental Science Center (K.I., A.G., Y.K., E.C., B.G., P.E.G.)
Division of Newborn Medicine (K.I., P.E.G.).
AJNR Am J Neuroradiol. 2017 Jul;38(7):1449-1455. doi: 10.3174/ajnr.A5217. Epub 2017 May 18.
Aberrant gyral folding is a key feature in the diagnosis of many cerebral malformations. However, in fetal life, it is particularly challenging to confidently diagnose aberrant folding because of the rapid spatiotemporal changes of gyral development. Currently, there is no resource to measure how an individual fetal brain compares with normal spatiotemporal variations. In this study, we assessed the potential for automatic analysis of early sulcal patterns to detect individual fetal brains with cerebral abnormalities.
Triplane MR images were aligned to create a motion-corrected volume for each individual fetal brain, and cortical plate surfaces were extracted. Sulcal basins were automatically identified on the cortical plate surface and compared with a combined set generated from 9 normal fetal brain templates. Sulcal pattern similarities to the templates were quantified by using multivariate geometric features and intersulcal relationships for 14 normal fetal brains and 5 fetal brains that were proved to be abnormal on postnatal MR imaging. Results were compared with the gyrification index.
Significantly reduced sulcal pattern similarities to normal templates were found in all abnormal individual fetuses compared with normal fetuses (mean similarity [normal, abnormal], left: 0.818, 0.752; < .001; right: 0.810, 0.753; < .01). Altered location and depth patterns of sulcal basins were the primary distinguishing features. The gyrification index was not significantly different between the normal and abnormal groups.
Automated analysis of interrelated patterning of early primary sulci could outperform the traditional gyrification index and has the potential to quantitatively detect individual fetuses with emerging abnormal sulcal patterns.
脑回折叠异常是许多脑畸形诊断的关键特征。然而,在胎儿期,由于脑回发育的快速时空变化,准确诊断异常折叠极具挑战性。目前,尚无资源可用于衡量个体胎儿大脑与正常时空变化的差异。在本研究中,我们评估了自动分析早期脑沟模式以检测患有脑部异常的个体胎儿大脑的潜力。
对三平面磁共振图像进行对齐,为每个个体胎儿大脑创建运动校正后的容积,并提取皮质板表面。在皮质板表面自动识别脑沟盆地,并与从9个正常胎儿脑模板生成的组合集进行比较。使用多变量几何特征和脑沟间关系对14个正常胎儿脑和5个在出生后磁共振成像中被证实异常的胎儿脑的脑沟模式与模板的相似性进行量化。将结果与脑回化指数进行比较。
与正常胎儿相比,所有异常个体胎儿的脑沟模式与正常模板的相似性均显著降低(平均相似性[正常,异常],左侧:0.818,0.752;<.001;右侧:0.810,0.753;<.01)。脑沟盆地位置和深度模式的改变是主要的区别特征。正常组和异常组之间的脑回化指数无显著差异。
早期主要脑沟相互关联模式的自动分析可能优于传统的脑回化指数,并且有潜力定量检测出具有异常脑沟模式的个体胎儿。