# Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction

Source: [Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction](<https://www.sota2.com/research/paper/68fa7b451e209131cea876fd>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-02-14T05:00:04.348Z

Authors: Thomas Sanchez, Vladyslav Zalevskyi, Angeline Mihailov, Gerard Mart\\'i-Juan, Elisenda Eixarch, Andras Jakab, Vincent Dunet, M\\'eriam Koob, Guillaume Auzias, Meritxell Bach Cuadra

Published: 2025-03-13T08:34:40.000Z

## 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 acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processing step where multiple stacks of thick 2D slices are registered together and combined to build a single, isotropic and artifact-free T2 weighted volume. We propose FetMRQC$\_{SR}$, a machine-learning method that extracts more than 100 image quality metrics to predict image quality scores using a random forest model. This approach is well suited to a problem that is high dimensional, with highly heterogeneous data and small datasets. We validate FetMRQC$\_{SR}$ in an out-of-domain (OOD) setting and report high performance (ROC AUC = 0.89), even when faced with data from an unknown site or SRR method. We also investigate failure cases and show that they occur in $45\\%$ of the images due to ambiguous configurations for which the rating from the expert is arguable. These results are encouraging and illustrate how a non deep learning-based method like FetMRQC$\_{SR}$ is well suited to this multifaceted problem. Our tool, along with all the code used to generate, train and evaluate the model are available at https://github.com/Medical-Image-Analysis-Laboratory/fetmrqc\_sr/ .

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Quality Control on Fetal brain MRI SRR (Leave-one-SRR-out, out-of-domain)](<https://www.sota2.com/research/sota/quality-control-on-fetal-brain-mri-srr-leave-one-srr-out-out-of-domain.md>): Quality Control; Fetal brain MRI SRR Leave-one-SRR-out (out-of-domain); AUC: 91.

- [Quality Control on Fetal brain MRI SRR (Leave-one-site-out)](<https://www.sota2.com/research/sota/quality-control-on-fetal-brain-mri-srr-leave-one-site-out.md>): Quality Control; Fetal brain MRI SRR Leave-one-site-out (out-of-domain); AUC: 92.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2503.10156>)

[PDF](<https://arxiv.org/pdf/2503.10156.pdf>)