Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.
Department of Radiology, School of Medicine, Women's Hospital, Zhejiang University, Hangzhou, Zhejiang, China.
Brain Struct Funct. 2021 Jul;226(6):1961-1972. doi: 10.1007/s00429-021-02303-x. Epub 2021 May 29.
Fetal brain MRI has become an important tool for in utero assessment of brain development and disorders. However, quantitative analysis of fetal brain MRI remains difficult, partially due to the limited tools for automated preprocessing and the lack of normative brain templates. In this paper, we proposed an automated pipeline for fetal brain extraction, super-resolution reconstruction, and fetal brain atlasing to quantitatively map in utero fetal brain development during mid-to-late gestation in a Chinese population. First, we designed a U-net convolutional neural network for automated fetal brain extraction, which achieved an average accuracy of 97%. We then generated a developing fetal brain atlas, using an iterative linear and nonlinear registration approach. Based on the 4D spatiotemporal atlas, we quantified the morphological development of the fetal brain between 23 and 36 weeks of gestation. The proposed pipeline enabled the fully automated volumetric reconstruction for clinically available fetal brain MRI data, and the 4D fetal brain atlas provided normative templates for the quantitative characterization of fetal brain development, especially in the Chinese population.
胎儿脑 MRI 已成为评估胎儿脑发育和疾病的重要工具。然而，胎儿脑 MRI 的定量分析仍然具有挑战性，部分原因是缺乏用于自动预处理的工具以及缺乏规范化的脑模板。本文提出了一种自动流水线，用于对胎儿脑进行提取、超分辨率重建和胎儿脑图谱绘制，以定量描绘中国人群中妊娠中期至晚期的胎儿脑发育情况。首先，我们设计了一个 U-net 卷积神经网络，用于自动提取胎儿脑，平均准确率达到 97%。然后，我们使用迭代线性和非线性配准方法生成了一个发育中的胎儿脑图谱。基于 4D 时空图谱，我们量化了妊娠 23 至 36 周之间胎儿脑的形态发育。所提出的流水线能够对临床可用的胎儿脑 MRI 数据进行全自动容积重建，4D 胎儿脑图谱为胎儿脑发育的定量特征提供了规范化模板，尤其是在中国人群中。
