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12 results for “Fetal MRI”
Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
<p>This dataset contains the data used in the paper</p> <blockquote> <p>de Dumast, P., Sanchez, T., Lajous, H., Bach Cuadra, M. (2023). Simulation-Based Parameter Optimization for Fetal Brain MRI Super-Resolution Reconstruction. MICCAI 2023. LNCS, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_32</p> </blockquote> <p>A preprint can also be found on <a href="https://arxiv.org/abs/2211.14274">arXiv</a>. If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This paper studied the impact of the regularization parameter <span class="math-tex">\(\alpha \)</span> on the super-resolution reconstruction of fetal brain magnetic resonance (MR) images. It used simulated T2-weighted data MR images generated using FaBiAN v2.0, a Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates fast spin echo (FSE) sequences of the developing fetal brain throughout gestation. The dataset contains the raw simulated data, the corresponding ground truths as well as corresponding super-resolution (SR) reconstructions using MIALSRTK and NiftyMIC with varying regularization parameters <span class="math-tex">\(\alpha \)</span>.</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p>
Dataset - FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control
<p>This dataset contains the data and model used in the paper</p> <blockquote> <p>Thomas Sanchez, Oscar Esteban, Yvan Gomez, Alexandre Pron, Mériam Koob, Vincent Dunet, Nadine Girard, Andras Jakab, Elisenda Eixarch, Guillaume Auzias, and Meritxell Bach Cuadra. "FetMRQC: an open-source machine learning framework for multi-centric fetal brain MRI quality control." <a href="https://arxiv.org/abs/2311.04780"><em>arXiv preprint arXiv:2311.04780</em></a> (2023).</p> </blockquote> <p>If you found this dataset useful or used it in your research, please cite this reference.</p> <p>This dataset contains manual quality annotations and image quality metrics (IQMs) obtained from 1647 stacks of T2-weighted (T2w) slices of fetal brain magnetic resonance (MR) images collected from 233 subjects at four different institutions Lausanne University Hospital (CHUV) in Switzerland, BCNatal at Hospital Sant Joan de Déu in Barcelona (Spain), University Children's Hospital Zürich (KISPI) in Switzerland and La Timone University Hospital in Marseille, France. The data were acquired on scanners from different vendors (Siemens at CHUV, BCNatal and Marseille, General Electrics at KISPI), MR sequences (Half Fourier Single-shot Turbo spin-Echo –HASTE– for Siemens scanners and Single-Short Fast Spin Echo –SS-FSE– for GE scanners), magnetic field strengths (1.5 T and 3 T), image resolutions, fields of view, repetition times and echo times, with both neurotypical and pathological cases.</p> <p>These data and the derived IQMs were used to train and evaluate models for quality assessment and quality control of fetal brain MR images. The code to reproduce the experiments is available on <a href="https://github.com/Medical-Image-Analysis-Laboratory/fetal_brain_qc">GitHub.</a></p> <p>Each entry describe the information for a single stack of T2w slices. It contains information regarding which subject it belongs to, its manual quality rating, scanner-related information and 332 IQMs, starting at the `centroid` column in the file. Further description of the data is available in the materials and methods section of the <a href="https://arxiv.org/abs/2311.04780">paper</a>.</p> <p>The model is a 2D nnUNet [1] segmentation network trained on the super-resolution reconstructed data and manual segmentations available as part of the<a href="https://www.synapse.org/#!Synapse:syn25649159/wiki/610007"> Fetal Tissue Annotation Challenge</a> (FeTA).</p> <p>Copyright (c) - All rights reserved. Medical Image Analysis Laboratory - Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland & CIBM Center for Biomedical Imaging. 2023.</p> <p>[1] Isensee, Fabian, et al. "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation." <em>Nature methods</em> 18.2 (2021): 203-211.</p>
Dataset: Quantitative Evaluation of Enhanced multi-plane clinical fetal diffusion MRI with a crossing-fiber phantom
<p>This dataset provides MRI acquisitions of a customized crossing phantom for fetal brain. It contains:<br> <br> 1) High Resolution acquisitions of 1.5 mm<sup>3</sup> isotropic and 61 directions (with b-vectors/b-values)</p> <p>2) Six low resolution acquisitions of 1x1x4 mm<sup>3</sup> and 9-16-25 directions (with respective b-vectors/b-values)</p> <p>3) A structural T2-w acquisition</p>
MRI Versus Four Dimensional Ultrasound in Detection of CNS Fetal Congenital Anomalies
ClinicalTrials.gov study NCT03888794. IPD Sharing: UNDECIDED. Countries: 0. Publications: 3.
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.
The Value of Advanced Imaging Sequences for Fetal MRI in Clinical Practice
ClinicalTrials.gov study NCT03302663. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Normative Brain Volume Profiles From Multicenter Fetal MRI
ClinicalTrials.gov study NCT06081036. IPD Sharing: NO. Countries: 1. Publications: 0.
Utility of MRI With Doppler Ultrasound Gating for Advanced Fetal Monitoring
ClinicalTrials.gov study NCT03541291. IPD Sharing: NO. Countries: 1. Publications: 0.
Prediction of Adverse Outcome Using Fetal MRI in Pregnancies at Risk of Preterm Birth
ClinicalTrials.gov study NCT05164432. IPD Sharing: YES. Countries: 1. Publications: 0.
Fetal MRI Acquisition and Sequence Development
ClinicalTrials.gov study NCT05253742. IPD Sharing: NO. Countries: 0. Publications: 0.
Effects of labelling fetal MSC with MRI Particles: MGIO and Ferucarbotran
GEO Series GSE13975. Homo sapiens. 9 samples. Type: Expression profiling by array.
Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation
<p><strong>Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation</strong></p> <p>This repository contains data contributed and used in our MICCAI 2021 paper:<br> Fidon, L. et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation.</p> <p>Our code is publicly available at<br> <a href="https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21">https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21</a></p> <p><strong>Files:</strong></p> <ul> <li>FeTA2021_Release1and2Corrected_v4.zip: contains 90 fetal brain T2w MRIs of the FeTA 2021 dataset with updated manual segmentations</li> <li>MICCAI21_partial_supervision_trained_models.zip: contains the weights of our pre-trained deep neural networks for fetal brain 3D MRI segmentation</li> </ul> <p>The FeTA 3D MRIs and segmentations contained in this repository shall be used only for research and education purposes.</p> <p>Segmentation labels:<br> 1: white matter (excluding corpus callosum; see below)<br> 2: intra-axial cerebrospinal fluid (CSF)<br> 3: cerebellum<br> 4: extra-axial CSF<br> 5: cortical gray matter<br> 6: deep gray matter<br> 7: brainstem<br> 8: corpus callosum</p> <p><strong>Updates:</strong></p> <p><strong>Version 4 (November 2022)</strong><br> 8 cases were forgotten by mistake when uploading version 3 (sub-feta004, sub-feta008, sub-feta017, sub-feta020, sub-feta021, sub-feta022, sub-feta023, sub-feta027). I have added them back in version 4, including the corpus callosum segmentations for those cases.<br> <br> Thank you to those who let me know about this. Please do not hesitate to reach out to me (lucas.fdon@gmail.com) if you think something is wrong.</p> <p><strong>Version 3 (July 2022)</strong></p> <p>Added some corpus callosum segmentations that were missing in version 2.</p> <p><strong>Version 2 (Avril 2022)</strong></p> <p>3D MRI and refined manual segmentation have been added for:<br> - the 10 fetal brain MRIs of the testing set of the FeTA data release 1 (sub-feta081 to sub-feta090).<br> - the 40 fetal brain MRIs new in the FeTA data release 2 (sub-041 to sub080).<br> The same pre-processing as in version 1 below has been performed.<br> The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Some additional refinements of the manual segmentation have been performed for sub-feta001 to sub-feta040.</p> <p>See participant.tsv for more information about the 3D MRI studies.</p> <p>The pathology for each study has been diagnosed by two radiologists in our team and can be found in participant.tsv.<br> Those are not present in the original FeTA dataset release 2.1</p> <p><strong>Version 1</strong><br> Summary of the changes done on the original FeTA dataset (data release 1) [1].</p> <p>The only modifications we did are detailed below (pre-processing and manual segmentation).</p> <p>Pre-processing:<br> -registration to the neurotypical fetal brain atlas of Gholipour et al [2]<br> -resampling to 0.8mm isotropic<br> -automatic mask via label propagation using a neurotypical [2] and a spina bifida [3] fetal brain atlas.</p> <p>The code used for the pre-processing is available at<br> https://github.com/LucasFidon/fetal-seg-preprocessing</p> <p>Segmentation:<br> Manual corrections of all manual segmentations by Michael Aertsen, Lucas Fidon, and Philippe Demaerel for the 38 3D MRIs in the FeTA dataset release 1.<br> For 2 cases, the segmentations were not corrected manually (see /Excluded).</p> <p>[1] 10.5281/zenodo.4541605<br> [2] http://crl.med.harvard.edu/research/fetal_brain_atlas/<br> [3] 10.7303/syn25887675</p> <p><strong>How to cite:</strong></p> <p>If you use the deep neural network weights please cite:</p> <ul> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p>if you use the FeTA data (release 1 and 2) and the corrected segmentations that we contributed please cite:</p> <ul> <li>Payette, K., de Dumast, P., Kebiri, H. et al. An automatic multi-tissue human fetal brain segmentation benchmark using the Fetal Tissue Annotation Dataset. Sci Data 8, 167 (2021). https://doi.org/10.1038/s41597-021-00946-3</li> <li>Fidon, L., Aertsen, M., Emam, D., et al. Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation. MICCAI (2021).</li> </ul> <p><strong>Terms of use - FeTA 3D MRIs and segmentations:</strong></p> <p>This is an agreement (“Agreement”) between you the downloader (“Downloader”) and the owner of the materials (“User”) governing the use of the Fetal Tissue Annotation and Segmentation Dataset to be downloaded.</p> <p>I. Acceptance of this Agreement</p> <p>By downloading or otherwise accessing the Fetal Tissue Annotation and Segmentation Dataset, the Downloader represents his/her acceptance of the terms of this Agreement.</p> <p>II. Data ownership</p> <p>The owner of the Fetal Tissue Annotation and Segmentation Dataset is the University Children’s Hospital Zurich.</p> <p>III. Use of the Materials</p> <p>Fetal Tissue Annotation and Segmentation Dataset is used only for research and education. Any other kind of use you will lead to the recall of all datasets, stop of collaboration, and legal consequences. This Agreement represents the entire agreement between Downloader and User with respect to the downloading and use of the Materials and supersedes all prior or contemporaneous communications and proposals (whether oral, written, or electronic) between Downloader and User with respect to downloading or using the Materials. </p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.