FetMRQC: Automated Quality Control for fetal brain MRI
This addresses the need for reliable neuroimaging in fetal brain studies, where motion artifacts are common, and is incremental as it builds on existing slice-level methods by using volume-level assessment.
The authors tackled the problem of automated quality control for fetal brain MRI, which is challenging due to motion artifacts, by proposing FetMRQC, a machine learning framework that predicts expert ratings and generalizes out-of-domain across two institutions with over 1000 low-resolution stacks.
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/ .