Hong Jinwoo, Yun Hyuk Jin, Park Gilsoon, Kim Seonggyu, Ou Yangming, Vasung Lana, Rollins Caitlin K, Ortinau Cynthia M, Takeoka Emiko, Akiyama Shizuko, Tarui Tomo, Estroff Judy A, Grant Patricia Ellen, Lee Jong-Min, Im Kiho
Department of Electronic Engineering, Hanyang University, Seoul, South Korea.
Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital and Harvard Medical School, Boston, MA, United States.
Front Neurosci. 2021 Oct 11;15:714252. doi: 10.3389/fnins.2021.714252. eCollection 2021.
The accurate prediction of fetal brain age using magnetic resonance imaging (MRI) may contribute to the identification of brain abnormalities and the risk of adverse developmental outcomes. This study aimed to propose a method for predicting fetal brain age using MRIs from 220 healthy fetuses between 15.9 and 38.7 weeks of gestational age (GA). We built a 2D single-channel convolutional neural network (CNN) with multiplanar MRI slices in different orthogonal planes without correction for interslice motion. In each fetus, multiple age predictions from different slices were generated, and the brain age was obtained using the mode that determined the most frequent value among the multiple predictions from the 2D single-channel CNN. We obtained a mean absolute error (MAE) of 0.125 weeks (0.875 days) between the GA and brain age across the fetuses. The use of multiplanar slices achieved significantly lower prediction error and its variance than the use of a single slice and a single MRI stack. Our 2D single-channel CNN with multiplanar slices yielded a significantly lower stack-wise MAE (0.304 weeks) than the 2D multi-channel (MAE = 0.979, < 0.001) and 3D (MAE = 1.114, < 0.001) CNNs. The saliency maps from our method indicated that the anatomical information describing the cortex and ventricles was the primary contributor to brain age prediction. With the application of the proposed method to external MRIs from 21 healthy fetuses, we obtained an MAE of 0.508 weeks. Based on the external MRIs, we found that the stack-wise MAE of the 2D single-channel CNN (0.743 weeks) was significantly lower than those of the 2D multi-channel (1.466 weeks, < 0.001) and 3D (1.241 weeks, < 0.001) CNNs. These results demonstrate that our method with multiplanar slices accurately predicts fetal brain age without the need for increased dimensionality or complex MRI preprocessing steps.
利用磁共振成像（MRI）准确预测胎儿脑龄可能有助于识别脑异常情况以及不良发育结局的风险。本研究旨在提出一种利用220例孕龄（GA）在15.9至38.7周之间的健康胎儿的MRI来预测胎儿脑龄的方法。我们构建了一个二维单通道卷积神经网络（CNN），使用不同正交平面的多平面MRI切片，且未对层间运动进行校正。在每个胎儿中，从不同切片生成多个年龄预测值，并使用在二维单通道CNN的多个预测值中确定最频繁值的众数来获得脑龄。我们在所有胎儿中获得的GA与脑龄之间的平均绝对误差（MAE）为0.125周（0.875天）。与使用单个切片和单个MRI堆栈相比，使用多平面切片实现了显著更低的预测误差及其方差。我们的具有多平面切片的二维单通道CNN产生的逐堆栈MAE（0.304周）显著低于二维多通道（MAE = 0.979，<0.001）和三维（MAE = 1.114，<0.001）CNN。我们方法的显著性图表明，描述皮质和脑室的解剖学信息是脑龄预测的主要贡献因素。将所提出的方法应用于21例健康胎儿的外部MRI时，我们获得的MAE为0.508周。基于外部MRI，我们发现二维单通道CNN的逐堆栈MAE（0.743周）显著低于二维多通道（1.466周，<0.001）和三维（1.241周，<0.001）CNN。这些结果表明，我们的多平面切片方法无需增加维度或复杂的MRI预处理步骤就能准确预测胎儿脑龄。
