mlsanityData to 13 Oct 2026

FetMRQC: a robust quality control system for multi-centric fetal brain MRI

arXiv 2311.04780 ↗First posted 8 Nov 2023eess.IV cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 13 Oct 2026). Builds on 18 of its 68 references that are in the map. First posted on arXiv 8 Nov 2023.

Builds on

18 of 68 references.

References · 18

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6 more
  1. 2022Labeling instructions matter in biomedical image analysiscited by 20
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  3. 2021MONAIfbs: MONAI-based fetal brain MRI deep learning segmentationcited by 12
  4. 2020Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistencycited by 10
  5. 2022An efficient semi-supervised quality control system trained using physics-based MRI-artefact generators and adversarial trainingcited by 3
  6. 2023FetMRQC: Automated Quality Control for fetal brain MRIcited by 2

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Cited by year (2026 to 13 Oct)

Citing papers per year: 2025: 3; 2026: 1.

4 papers.

  1. 2025Pathological MRI Segmentation by Synthetic Pathological Data Generation in Fetuses and Neonatescited by 1
  2. 2025Automatic quality control in multi-centric fetal brain MRI super-resolution reconstructioncited by 1
  3. 2025MMRQA: Signal-Enhanced Multimodal Large Language Models for MRI Quality Assessmentcited by 0
  4. 2026PRIME-SVR: Physics-infoRmed Implicit Multi-Echo Slice-to-Volume Reconstruction for Fetal T2 mappingcited by 0