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)是最有前途的脑研究工具。 微观结构。然而，dmri在评估宫内胎儿大脑方面的应用受到以下挑战。 不可预测的运动、低信噪比、低空间分辨率和成像伪影。虽然付出了很大努力 一直致力于改进图像采集和运动补偿技术、数据处理和 分析方法基本保持不变。现有生物标记物评估方法在胎儿磁共振成像中的应用 从低准确度和低重复性。此外，跨学科和人口研究需要划定 白质(WM)束，目前只能通过高度主观和耗时的方法进行 手动分段。这些缺点大大限制了我们在这个关键时刻研究大脑的能力 并检测由于疾病引起的大脑微结构的细微变化。这个拟议的项目将开发 并验证了新一代分析胎儿dMRI数据的方法。与现有方法不同，现有方法 基于扩散信号的生物物理模型和数学模型的拟合，新方法将依赖于 数据驱动和机器学习技术。建立在我们的开创性工作的基础上，这些工作展示了 这些方法，我们将开发深度学习技术来估计微结构生物标记物，如 分数各向异性、轴突取向分散和纤维取向分布。新的方法将是 基于两级变压器网络，将使用早产儿的dMRI数据和 胎儿。此外，我们将开发使用欠采样扫描的方法，并提供经过校准的 评估不确定度的度量。我们将开发卷积神经网络来分割WM区域 基于胎儿大脑局部纤维的定位。为了解决输入和目标标签中的噪声，我们将生成 在我们之前关于带噪声数据和标签的分割、形状感知分割以及使用 不确定性，以提高分割的准确性。这项新技术将自动生成区域，其中包括区域 它们与最优秀的人类专家创造的那些没有什么区别。我们将对新方法进行评估 使用测试-重测和自举方法并通过专家对胎儿大脑微结构和 具有瞬时胎儿纤维通路的组织学知识。新的方法将能够评估胎儿 脑微结构与神经发育障碍对脑束特异性微结构的影响 精确度、细节和重现性，这是目前无法企及的。以明确地展示其价值和 新方法的意义，我们将使用它们来评估由于以下原因引起的WM微观结构的变化 先天性心脏病(CHD)，是最常见的出生缺陷。在这个过程中，我们将生产最多的 对先心病对胎儿大脑微结构影响的全面而详细的描述。

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.