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.

中文翻译：

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FetMRQC：用于多中心胎儿脑 MRI 的强大质量控制系统


胎儿大脑 MRI 正在成为围产期诊断中神经超声检查日益相关的补充，可以从根本上了解整个妊娠期胎儿大脑的发育。然而，不受控制的胎儿运动和采集协议中的异质性导致数据质量参差不齐，可能使后续研究的结果产生偏差。我们提出了 FetMRQC，这是一种用于自动图像质量评估和质量控制的开源机器学习框架，该框架对于临床数据异质性引起的领域转移具有鲁棒性。 FetMRQC 从未经处理的解剖 MRI 中提取质量指标集合，并将它们结合起来，使用随机森林预测专家的评分。我们在一个庞大且多样化的数据集上验证了我们的框架，该数据集包含来自四个临床中心和 13 个不同扫描仪的 1600 多个手动评分的胎儿大脑 T2 加权图像。我们的研究表明，FetMRQC 的预测可以很好地推广到未见过的数据，同时具有可解释性。 FetMRQC 是朝着更强大的胎儿大脑神经成像迈出的一步，它有可能为人类大脑的发育提供新的见解。