Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where large and unpredictable fetal motion can lead to substantial artifacts in the acquired images. Existing methods for fetal brain quality assessment operate at the \textit{slice} level, and fail to get a comprehensive picture of the quality of an image, that can only be achieved by looking at the \textit{entire} brain volume. In this work, we propose FetMRQC, a machine learning framework for automated image quality assessment tailored to fetal brain MRI, which extracts an ensemble of quality metrics that are then used to predict experts' ratings. Based on the manual ratings of more than 1000 low-resolution stacks acquired across two different institutions, we show that, compared with existing quality metrics, FetMRQC is able to generalize out-of-domain, while being interpretable and data efficient. We also release a novel manual quality rating tool designed to facilitate and optimize quality rating of fetal brain images. Our tool, along with all the code to generate, train and evaluate the model is available at https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc/ .

翻译：质量控制（QC）长期以来被视为确保神经影像学研究可靠性的关键，对于胎儿脑部MRI尤为重要。由于胎儿运动具有大幅度和不可预测性，获取的图像中常出现显著伪影。现有胎儿脑部质量评估方法仅限于单层（slice）级别，无法全面评估图像质量——这一目标仅能通过分析整个脑部容积（entire brain volume）实现。本文提出FetMRQC，一种面向胎儿脑部MRI的机器学习自动图像质量评估框架，该框架提取一组质量指标以预测专家评分。基于来自两家不同机构的1000余张低分辨率堆叠图像的人工评分，我们证明FetMRQC相较于现有质量指标，在保持可解释性和数据效率的同时，具备跨领域泛化能力。此外，我们发布了一款新型人工质量评分工具，旨在简化和优化胎儿脑部图像的质量评级。该工具及用于生成、训练和评估模型的所有代码详见https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc/。