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.

{"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"]}

This work is supported by the Swiss National Science Foundation through grants 182602 and 141283, and by the Eranet Neuron MULTIFACT project (SNSF 31NE30 203977). 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 CHUV, UNIL, EPFL, UNIGE and HUG.