Fetal brain MRI is becoming an increasingly relevant complement to neurosonography for perinatal diagnosis, allowing fundamental insights into fetal brain development throughout gestation. However, uncontrolled fetal motion and heterogeneity in acquisition protocols lead to data of variable quality, potentially biasing the outcome of subsequent studies. We present FetMRQC, an open-source machine-learning framework for automated image quality assessment and quality control that is robust to domain shifts induced by the heterogeneity of clinical data. FetMRQC extracts an ensemble of quality metrics from unprocessed anatomical MRI and combines them to predict experts' ratings using random forests. We validate our framework on a pioneeringly large and diverse dataset of more than 1600 manually rated fetal brain T2-weighted images from four clinical centers and 13 different scanners. Our study shows that FetMRQC's predictions generalize well to unseen data while being interpretable. FetMRQC is a step towards more robust fetal brain neuroimaging, which has the potential to shed new insights on the developing human brain.

翻译：胎儿脑部磁共振成像正成为围产期诊断中与神经超声学日益相关的重要补充手段，有助于深入理解整个妊娠期胎儿脑部发育过程。然而，无法控制的胎儿运动以及采集方案的非同质性导致数据质量参差不齐，可能对后续研究结果产生偏差。我们提出了FetMRQC——一种开源机器学习框架，用于自动化图像质量评估和质量控制，该框架对临床数据非同质性引起的域偏移具有稳健性。FetMRQC从未经处理的解剖学MRI中提取一组质量指标，并利用随机森林将其结合以预测专家的评分。我们基于一个具有开创性大规模且多样化的数据集验证了该框架，该数据集包含来自四个临床中心和13种不同扫描仪的超过1600张手动评分的胎儿脑部T2加权图像。研究表明，FetMRQC的预测在可解释的前提下对新数据具有良好的泛化能力。FetMRQC是迈向更稳健的胎儿脑部神经影像学的一步，有望为发育中的人类大脑提供新的见解。