Titre
A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).
Type
article
Institution
UNIL/CHUV/Unisanté + institutions partenaires
Périodique
Auteur(s)
Lajous, H.
Auteure/Auteur
Roy, C.W.
Auteure/Auteur
Hilbert, T.
Auteure/Auteur
de Dumast, P.
Auteure/Auteur
Tourbier, S.
Auteure/Auteur
Alemán-Gómez, Y.
Auteure/Auteur
Yerly, J.
Auteure/Auteur
Yu, T.
Auteure/Auteur
Kebiri, H.
Auteure/Auteur
Payette, K.
Auteure/Auteur
Ledoux, J.B.
Auteure/Auteur
Meuli, R.
Auteure/Auteur
Hagmann, P.
Auteure/Auteur
Jakab, A.
Auteure/Auteur
Dunet, V.
Auteure/Auteur
Koob, M.
Auteure/Auteur
Kober, T.
Auteure/Auteur
Stuber, M.
Auteure/Auteur
Bach Cuadra, M.
Auteure/Auteur
Liens vers les unités
ISSN
2045-2322
Statut éditorial
Publié
Date de publication
2022-05-23
Volume
12
Numéro
1
Première page
8682
Peer-reviewed
Oui
Langue
anglais
Notes
Publication types: Journal Article
Publication Status: epublish
Publication Status: epublish
Résumé
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.
PID Serval
serval:BIB_9F910B893D7D
PMID
Open Access
Oui
Date de création
2022-05-31T06:45:00.358Z
Date de création dans IRIS
2025-05-21T03:18:01Z
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Nom
35606398_BIB_9F910B893D7D.pdf
Version du manuscrit
published
Licence
https://creativecommons.org/licenses/by/4.0
Taille
2.63 MB
Format
Adobe PDF
PID Serval
serval:BIB_9F910B893D7D.P001
URN
urn:nbn:ch:serval-BIB_9F910B893D7D3
Somme de contrôle
(MD5):4f262593adc03bd5ea166f760aac6f84