  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.
