Abstract
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 slice level, and fail to get a comprehensive picture of the quality of an image, that can only be achieved by looking at the 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/.
TS is supported by the Era-net Neuron MULTIFACT project (SNSF 31NE30_203977), OE is supported by the Swiss National Science Foundation (SNSF #185872), the NIMH (RF1MH12186), and the CZI (EOSS5/‘NiPreps’). YG acknowledges support from the SICPA foundation and EE is supported by the Instituto de Salud Carlos III (ISCIII) (AC21_2/00016). We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by CHUV, UNIL, EPFL, UNIGE and HUG.
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Notes
- 1.
We use the pre-trained models throughout these experiments, as we want to test the off-the-shelf value of these IQMs.
- 2.
The method of Liao et al. [22] was not included because their code is not publicly available, and we could not get in contact with the authors.
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Sanchez, T., Esteban, O., Gomez, Y., Eixarch, E., Cuadra, M.B. (2023). FetMRQC: Automated Quality Control for Fetal Brain MRI. In: Link-Sourani, D., Abaci Turk, E., Macgowan, C., Hutter, J., Melbourne, A., Licandro, R. (eds) Perinatal, Preterm and Paediatric Image Analysis. PIPPI 2023. Lecture Notes in Computer Science, vol 14246. Springer, Cham. https://doi.org/10.1007/978-3-031-45544-5_1
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