ArticleScientific reports2022
A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 21 citations in OpenAlex.
- Roles and applications of artificial intelligence in fetal and placental MRI: a literature review.BMC pregnancy and childbirth · 2026Pooled it
- Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions.Pediatric radiology · 2026Review
- Orientation-Aware Diffusion Super-Resolution for 3T-Like Fetal MRI from Routine 1.5T Scans.Proceedings of machine learning research · 2026Article
- Toward structured fellowships in fetal neuroimaging.Acta obstetricia et gynecologica Scandinavica · 2025Article
- Variable Flip Angle Optimization for Fetal Brain Imaging With Reduced Specific Absorption Rate.NMR in biomedicine · 2025Article
- A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.Scientific data · 2025Article
- Multi-Center Fetal Brain Tissue Annotation (FeTA) Challenge 2022 Results.IEEE transactions on medical imaging · 2025Article
- SNAKE: A modular realistic fMRI data simulator from the space-time domain to k-space and back.Imaging neuroscience (Cambridge, Mass.) · 2025Article
- In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences.Magnetic resonance in medicine · 2024Article
- A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).Scientific reports · 2022Article
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Authors and funding
19 authors at 4 institutions in 1 country.
Funding
Abstract
Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.