Diffusion-weighted magnetic resonance imaging (DW-MRI) of fetal brain is challenged by frequent fetal motion and signal to noise ratio that is much lower than non-fetal imaging. As a result, accurate and robust parameter estimation in fetal DW-MRI remains an open problem. Recently, deep learning techniques have been successfully used for DW-MRI parameter estimation in non-fetal subjects. However, none of those prior works has addressed the fetal brain because obtaining reliable fetal training data is challenging. To address this problem, in this work we propose a novel methodology that utilizes fetal scans as well as scans from prematurely-born infants. High-quality newborn scans are used to estimate accurate maps of the parameter of interest. These parameter maps are then used to generate DW-MRI data that match the measurement scheme and noise distribution that are characteristic of fetal data. In order to demonstrate the effectiveness and reliability of the proposed data generation pipeline, we used the generated data to train a convolutional neural network (CNN) to estimate color fractional anisotropy (CFA). We evaluated the trained CNN on independent sets of fetal data in terms of reconstruction accuracy, precision, and expert assessment of reconstruction quality. Results showed significantly lower reconstruction error ($n=100,p<0.001$) and higher reconstruction precision ($n=20,p<0.001$) for the proposed machine learning pipeline compared with standard estimation methods. Expert assessments on 20 fetal test scans showed significantly better overall reconstruction quality ($p<0.001$) and more accurate reconstruction of 11 regions of interest ($p<0.001$) with the proposed method.

胎儿大脑的弥散加权磁共振成像（DW-MRI）面临频繁的胎儿运动和远低于非胎儿成像的信噪比的挑战。因此，胎儿 DW-MRI 中准确且稳健的参数估计仍然是一个悬而未决的问题。最近，深度学习技术已成功用于非胎儿受试者的 DW-MRI 参数估计。然而，这些先前的工作都没有涉及胎儿大脑，因为获得可靠的胎儿训练数据具有挑战性。为了解决这个问题，在这项工作中，我们提出了一种利用胎儿扫描以及早产婴儿扫描的新颖方法。高质量的新生儿扫描用于估计感兴趣参数的准确图。然后使用这些参数图生成与测量方案和胎儿数据特征的噪声分布相匹配的 DW-MRI 数据。为了证明所提出的数据生成管道的有效性和可靠性，我们使用生成的数据来训练卷积神经网络（CNN）来估计颜色分数各向异性（CFA）。我们在独立的胎儿数据集上评估了经过训练的 CNN 的重建准确性、精确度和重建质量的专家评估。结果显示重建误差显着降低（$n=100,p<0.001$）和更高的重建精度（$n=20,p<0.001$）将所提出的机器学习流程与标准估计方法进行比较。对 20 次胎儿测试扫描的专家评估显示整体重建质量显着提高（$p<0.001$）以及 11 个感兴趣区域的更准确重建（$p<0.001$）与所提出的方法。