Data quality biases normative models derived from fetal brain MRI

Abstract

Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus in utero. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st–10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of scans used to fit the models. Quality-induced bias was structure dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.

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Cite

BibTeX RIS

BibTeX

@article{sanchez2026data,
  title = {{Data quality biases normative models derived from fetal brain MRI}},
  author = {Thomas Sanchez and Angeline Mihailov and Gerard Martí-Juan and Nadine Girard and Aurélie Manchon and Mathieu Milh and Elisenda Eixarch and Vincent Dunet and Mériam Koob and Léo Pomar and Joanna Sichitiu and Miguel González Ballester and Oscar Camara and Gemma Piella and Meritxell Bach Cuadra and Guillaume Auzias},
  journal = {Imaging Neuroscience},
  year = {2026},
  month = aug,
  doi = {10.1162/imag.a.1337},
  url = {https://amu.hal.science/hal-05798304v1},
  note = {CC BY},
}

RIS

TY  - JOUR
TI  - Data quality biases normative models derived from fetal brain MRI
AU  - Thomas Sanchez
AU  - Angeline Mihailov
AU  - Gerard Martí-Juan
AU  - Nadine Girard
AU  - Aurélie Manchon
AU  - Mathieu Milh
AU  - Elisenda Eixarch
AU  - Vincent Dunet
AU  - Mériam Koob
AU  - Léo Pomar
AU  - Joanna Sichitiu
AU  - Miguel González Ballester
AU  - Oscar Camara
AU  - Gemma Piella
AU  - Meritxell Bach Cuadra
AU  - Guillaume Auzias
PY  - 2026
DA  - 2026/08/14
JO  - Imaging Neuroscience
DO  - 10.1162/imag.a.1337
UR  - https://amu.hal.science/hal-05798304v1
AB  - Normative modeling is increasingly used to characterize typical growth trajectories and identify atypical neurodevelopment, including early brain development using magnetic resonance imaging (MRI) acquired before birth. Recent work has emphasized the importance of large sample sizes for accurate and robust centile estimation. In this study, we investigate how image quality influences fetal brain normative models, a critical factor in this context where MRI is acquired on a moving fetus in utero. Using a multi-centric cohort of 635 fetal MRI scans, we applied a standardized visual quality control (QC) protocol with continuous quality ratings. We fit normative models for multiple brain structures under progressively relaxed QC stringency, and quantified the deviations in centile estimates relative to a high-quality reference subgroup. Our results showed that including lower-quality data systematically biased normative centiles, with the strongest effects observed in the outer centiles, particularly the lower tail (1st–10th). Bias increased progressively as QC stringency was relaxed and could not be attributed solely to the number of scans used to fit the models. Quality-induced bias was structure dependent, and often not visually apparent at the segmentation level. These findings highlight that image quality is an important source of bias in normative fetal brain modeling, and that increasing sample size at the expense of quality may systematically affect centile estimates, potentially jeopardizing the utility of the model.
SN  - 2837-6056
ER  -

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