MRI Brain Perturbations

We have uploaded MRI brain perturbations for data generation in Back-Projection Diffusion. The Brain MRI images used as our perturbations were obtained from the NYU fastMRI Initiative database (fastmri.med.nyu.edu), as described in the works of Florian Knoll et al., fastmri: A publicly available raw k-space and dicom dataset of knee images for accelerated mr image reconstruction using machine learning, 2020, and Jure Zbontar et al., fastmri: An open dataset and benchmarks for accelerated mri, 2019. As such, NYU fastMRI investigators provided the data but did not participate in the analysis or writing of this report. A listing of NYU fastMRI investigators, subject to updates, can be found at fastmri.med.nyu.edu. The primary goal of fastMRI is to test whether machine learning can aid in the reconstruction of medical images. We padded, resized, and normalized the perturbations to a native resolution of 240 points. Then, we downsampled the perturbations to resolutions of 60, 80, 120, and 160. We make the MRI brain perturbations publicly available. They are stored as HDF5 files with filenames in the format eta-n.h5, where n corresponds to the resolution. The files have the following structure: eta-n.h5/ ├── /eta For a formal description of the dataset, please refer to our preprint: Zhang, B., Guerra, M., Li, Q., & Zepeda-Núñez, L. (2025). Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models. Computer Methods in Applied Mechanics and Engineering, 443, 118036. https://doi.org/10.1016/j.cma.2025.118036 For usage instructions for Back-Projection Diffusion, please refer to our GitHub repository. If this dataset is useful to your research, please cite our preprint:@article{ZHANG2025118036,title = {Back-Projection Diffusion: Solving the wideband inverse scattering problem with diffusion models},journal = {Computer Methods in Applied Mechanics and Engineering},volume = {443},pages = {118036},year = {2025},issn = {0045-7825},doi = {https://doi.org/10.1016/j.cma.2025.118036},url = {https://www.sciencedirect.com/science/article/pii/S0045782525003081},author = {Borong Zhang and Martin Guerra and Qin Li and Leonardo Zepeda-Núñez},keywords = {Machine learning, Inverse scattering, Generative modeling, Wave propagation, Diffusion models}}