Enabling the Assessment of Fetal Brain Development and Degeneration with Machine Learning

项目摘要 弥散加权磁共振成像（dMRI）是研究脑的最有前途的工具 微观结构然而，dMRI在宫内胎儿脑评估中的应用受到以下因素的挑战： 不可预测的运动、低信噪比、低空间分辨率和成像伪影。虽然很多努力 一直致力于改善图像采集和运动补偿技术，数据处理， 分析方法基本保持不变。胎儿dMRI中现有的生物标志物估计方法受到 从低精度和低再现性。此外，跨学科和人口研究需要界定 白色物质（WM）束，目前只能通过高度主观和耗时的 手动分割。这些缺点极大地限制了我们在这个关键时刻研究大脑的能力。 阶段，并检测由于疾病引起的大脑微观结构的细微变化。该项目将开发 并验证新一代胎儿dMRI数据分析方法。与现有方法不同， 新方法基于扩散信号的生物物理模型和数学模型拟合， 数据驱动和机器学习技术。在我们的开创性工作的基础上， 这些方法，我们将开发深度学习技术，用于估计微观结构生物标志物， 分数各向异性、神经突取向分散和纤维取向分布。新方法将是 基于两阶段Transformer网络，该网络将使用早产儿的dMRI数据进行训练， 胎儿此外，我们将开发与欠采样扫描一起工作的方法，并提供校准的 估计不确定性的度量。我们将开发卷积神经网络来分割WM束， 胎儿大脑的基础上的局部纤维取向。为了解决输入和目标标签中的噪声，我们将构建 关于我们之前在噪声数据和标签分割、形状感知分割以及使用 不确定性，以提高分割精度。这项新技术将自动生成纤维束， 与最优秀的人类专家所创造的无法区分。我们将评估新方法 使用重测和自举方法，并通过专家对胎儿大脑微观结构的评估， 暂时性胎儿纤维通路的组织学知识。新的方法将使评估胎儿 大脑微观结构和神经发育障碍对特定区域微观结构的影响 准确性、细节和可重复性是目前无法达到的。为了明确地证明价值， 新方法的重要性，我们将使用它们来评估WM微观结构的改变， 先天性心脏病（CHD），这是最常见的出生缺陷。在这个过程中，我们将生产最 全面和详细的图片的影响，冠心病对胎儿的大脑微观结构曾经试图。

Project Summary Diffusion-weighted magnetic resonance imaging (dMRI) is the most promising tool for studying brain microstructure. However, the application of dMRI to the assessment of fetal brain in-utero is challenged by unpredictable motion, low signal-to-noise ratio, low spatial resolution, and imaging artifacts. While much effort has been spent on improving image acquisition and motion compensation techniques, data processing and analysis methods have remained largely unchanged. Existing biomarker estimation methods in fetal dMRI suffer from low accuracy and low reproducibility. Moreover, cross-subject and population studies require delineation of white matter (WM) tracts, which currently can only be performed via highly subjective and time-consuming manual segmentation. These shortcomings have significantly limited our ability to study the brain at this critical stage and to detect subtle changes in brain microstructure due to disorders. This proposed project will develop and validate a new generation of methods for analysis of fetal dMRI data. Unlike existing methods, which are based on biophysical models of the diffusion signal and mathematical model fitting, the new methods will rely on data-driven and machine learning techniques. Building on our pioneering works that have shown the potential of these methods, we will develop deep learning techniques for estimating microstructural biomarkers such as fractional anisotropy, neurite orientation dispersion, and fiber orientation distribution. The new methods will be based on two-stage transformer networks, which will be trained using dMRI data from preterm infants and fetuses. Moreover, we will develop methods that work with undersampled scans and provide a calibrated measure of estimation uncertainty. We will develop convolutional neural networks to segment WM tracts in the fetal brain based on the local fiber orientations. To address the noise in the input and target labels, we will build on our prior works on segmentation with noisy data and labels, shape-aware segmentation, and use of uncertainty to improve segmentation accuracy. The new technique will generate tracts automatically, with tracts that are indistinguishable from those created by the best human experts. We will evaluate the new methods using test-retest and bootstrapping methods and via assessment by experts in fetal brain microstructure and with histological knowledge of transient fetal fiber pathways. The new methods will enable assessment of fetal brain microstructure and the impact of neurodevelopmental disorders on tract-specific microstructure with a level of accuracy, detail, and reproducibility that is currently beyond reach. To definitively demonstrate the value and significance of the new methods, we will use them to assess the alterations in WM micro-structure due to congenital heart disease (CHD), which is the most common birth defect. In the process, we will produce the most comprehensive and detailed picture of the impact of CHD on the fetal brain microstructure ever attempted.