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4 results for “Fetal magnetic resonance imaging”
Virtual cohort of fetal cases with suspected coarctation of the aorta from fetal three-dimensional magnetic resonance images
<p>The present dataset is a supplementary dataset to the paper: Hermida, U., van Poppel, M.P.M., Lloyd, D.F.A. <em>et al.</em> Learning the Hidden Signature of Fetal Arch Anatomy: a Three-Dimensional Shape Analysis in Suspected Coarctation of the Aorta. <em>J. of Cardiovasc. Trans. Res.</em> (2022). https://doi.org/10.1007/s12265-022-10335-9</p> <p><strong>Dataset Description: </strong></p> <p>We present a database of fetal great arch anatomical models derived from a statistical shape model (SSM) of fetuses with suspected coarctation of the aorta (CoA). The dataset consists of 1000 meshes including the fetal ascending aorta, arterial duct and descending aorta. For each synthetic case, the corresponding PCA coefficients for the 10 first PCA modes, as well as their CoA risk score, are shared. PCA scores are provided both as raw scores and Z-scores. </p> <p>To build the SSM, a dataset of 112 motion-corrected 3D fetal cardiovascular magnetic resonance (CMR) images and their corresponding segmentations from cases with suspected CoA was used. Data included confirmed CoA cases as well as false positive (FP) cases. Centerlines were extracted and aligned to build the SSM of the fetal great arches. </p> <p><strong>Source of funding</strong></p> <p>This work was supported by the Wellcome Trust IEH Award [102431] (iFIND project), by core funding from the Wellcome/EPSRC Centre for Medical Engineering [WT203148/Z/16/Z] and by the Rosetrees Trust [A2725]. The research was funded by the National Institute for Health Research (NIHR) Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London and supported by the NIHR Clinical Research Facility. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. D.F.A. Lloyd acknowledges support from the Rothschild Foundation (2020/2017). P. Lamata holds a Wellcome Trust Senior Research Fellowship (209450/Z/17/Z).</p>
Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain
<p>This dataset gathers synthetic-yet-highly-realistic T2-weighted magnetic resonance images (MRI) of the fetal brain based on the latest development of our prototype Fetal Brain magnetic resonance Acquisition Numerical phantom that now simulates local heterogeneities within white matter tissues throughout maturation (FaBiAN v2.0).<br>This dataset is associated with the following paper:</p> <p><strong>- Lajous H. et al. (2024) A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain.</strong> Submitted to Nature Scientific Data, Pre-print available https://doi.org/10.1101/2024.04.08.588566</p> <p>We propose this unique, extensive fetal MRI dataset of simulated standard clinical fast spin echo sequences in both healthy and pathological neurodevelopmental trajectories to address data scarcity in this sensitive population, and therefore support the continuous endeavor of the community to develop advanced post-processing methods as well as cutting-edge artificial intelligence models. Automated brain tissue annotations of the two-dimensional, low-resolution series as well as super-resolution (SR) reconstructions of the fetal brain volumes are also included.</p> <p><strong>Work using any of these data should cite the following references:</strong></p> <ul> <li>Lajous, H. et al. A dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Submitted to Nature Scientific Data (2024), https://doi.org/10.1101/2024.04.08.588566</li> <li>Lajous, H. et al. Dataset of synthetic, maturation-informed magnetic resonance images of the human fetal brain. Zenodo (2024). 10.5281/zenodo.10940427</li> <li>Lajous, H., le Boeuf Fló, A., Esteban, O. & Bach Cuadra, M. Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN v2.0 (2.0). Zenodo (2023), 10.5281/zenodo.5471094</li> </ul> <p>This work was supported by the Swiss National Science Foundation through grant 182602, and by the ProTechno Foundation. We acknowledge access to the facilities and expertise of the CIBM Center for Biomedical Imaging, a Swiss research center of excellence founded and supported by Lausanne University Hospital (CHUV), University of Lausanne (UNIL), Ecole Polytechnique Fédérale de Lausanne (EPFL), University of Geneva (UNIGE) and Geneva University Hospitals (HUG).</p> <p>Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2024.</p> <p>Note: <em>Terms of use for the original cohort (</em>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI, 2021<em>) are for research and education purposes only.</em></p>
Fetal Magnetic Resonance Imaging: Z-score Reference Ranges for a Large Patient Population in Normal Neuroimaging Measurement
ClinicalTrials.gov study NCT07004283. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Functional Evaluation of the Fetal Lung by Functional Magnetic Resonance Imaging - Blood Oxygenation Level Dependent (MRI-BOLD), in Congenital Diaphragmatic and Parietal Malformations
ClinicalTrials.gov study NCT04186039. IPD Sharing: NO. Countries: 1. Publications: 0.
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OpenNeuro
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