A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).

Details

Serval ID
serval:BIB_9F910B893D7D
Type
Article: article from journal or magazin.
Collection
Publications
Institution
Title
A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN).
Journal
Scientific reports
Author(s)
Lajous H., Roy C.W., Hilbert T., de Dumast P., Tourbier S., Alemán-Gómez Y., Yerly J., Yu T., Kebiri H., Payette K., Ledoux J.B., Meuli R., Hagmann P., Jakab A., Dunet V., Koob M., Kober T., Stuber M., Bach Cuadra M.
ISSN
2045-2322 (Electronic)
ISSN-L
2045-2322
Publication state
Published
Issued date
23/05/2022
Peer-reviewed
Oui
Volume
12
Number
1
Pages
8682
Language
english
Notes
Publication types: Journal Article
Publication Status: epublish
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.
Keywords
Brain/diagnostic imaging, Humans, Image Processing, Computer-Assisted/methods, Magnetic Resonance Imaging/methods, Magnetic Resonance Spectroscopy, Phantoms, Imaging
Pubmed
Web of science
Open Access
Yes
Funding(s)
Swiss National Science Foundation / 182602
Swiss National Science Foundation / 173129
European Commission
Create date
31/05/2022 8:45
Last modification date
06/06/2022 6:36
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