Dataset: Simulation-based parameter optimization for fetal brain MRI super-resolution reconstruction
- 1. Department of Diagnostic and Interventional Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; CIBM Center for Biomedical Imaging, Switzerland
- 2. CIBM Center for Biomedical Imaging, Switzerland; Department of Diagnostic and Interventional Radiology Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
Description
This dataset contains the data used in the paper
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
A preprint can also be found on arXiv. If you found this dataset useful or used it in your research, please cite this reference.
This paper studied the impact of the regularization parameter \(\alpha \) 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 \(\alpha \).
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.
Notes
Files
experiment1.zip
Additional details
Related works
- Is published in
- Conference paper: 10.1007/978-3-031-43990-2_32 (DOI)
Funding
- Advanced super-resolution reconstruction methods for quantitative magnetic resonance imaging of the developing fetal brain 205321_182602
- Swiss National Science Foundation
- Multicentric study of Fetal Abnormal Cortical Trajectory with standardised and privacy-preserving method on fetal MRI 31NE30_203977
- Swiss National Science Foundation
References
- Lajous, H., et al. (2021). Medical-Image-Analysis-Laboratory/FaBiAN: FaBiAN. Zenodo. https://doi.org/10.5281/zenodo.5471094
- Lajous, H., et al. (2022). A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN). Sci Rep 12, 8682. https://doi.org/10.1038/s41598-022-10335-4
- Tourbier, S., et al. (2015). An efficient total variation algorithm for super-resolution in fetal brain MRI with adaptive regularization. NeuroImage, 118, 584-597. https://doi.org/10.1016/j.neuroimage.2015.06.018
- Tourbier, S., et al. (2023). Medical-Image-Analysis-Laboratory/mialsuperresolutiontoolkit: MIAL Super-Resolution Toolkit v2.1.0 (v2.1.0). Zenodo. https://doi.org/10.5281/zenodo.7612119
- Ebner, M., et al. (2020). An automated framework for localization, segmentation and super-resolution reconstruction of fetal brain MRI. NeuroImage, 206, 116324. https://doi.org/10.1016/j.neuroimage.2019.116324