Donders Institute, Radboud University Medical Center, Nijmegen, The Netherlands.
Department of Psychiatry, University of Michigan, MI, Ann Arbor, USA.
Neuroinformatics. 2022 Jan;20(1):173-185. doi: 10.1007/s12021-021-09528-5. Epub 2021 Jun 15.
Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Leveraging our library of 1,241 manually traced fetal fMRI images from 207 fetuses, we trained a Convolutional Neural Network (CNN) that achieved excellent performance across two held-out test sets from separate scanners and populations. Furthermore, we unite the auto-masking model with additional fMRI preprocessing steps from existing software and provide insight into our adaptation of each step. This work represents an initial advancement towards a fully comprehensive, open-source workflow, with openly shared code and data, for fetal functional MRI data preprocessing.
胎儿静息态功能磁共振成像（rs-fMRI）已成为一种在出生前描述大脑发育的重要新方法。尽管这种方法发展迅速且应用广泛，但目前我们缺乏适用于解决此类数据类型固有独特挑战的神经影像学处理管道。在这里，我们解决了最具挑战性的处理步骤，即在数千个非稳定的 3D 脑体积中快速准确地将胎儿大脑与周围组织分离。利用我们从 207 名胎儿中手动追踪的 1,241 张胎儿 fMRI 图像库，我们训练了一个卷积神经网络（CNN），该网络在来自两个独立扫描仪和人群的两个独立测试集中表现出了出色的性能。此外，我们将自动掩模模型与现有软件的其他 fMRI 预处理步骤结合起来，并深入了解我们对每个步骤的改编。这项工作代表了朝着全面、开源的工作流程迈出的初步进展，该流程适用于胎儿功能磁共振成像数据预处理，并且公开共享代码和数据。
