Supplementary Data for the Paper: “Deep Learning for Restoring MPI System Matrices Using Simulated Training Data”

This dataset provides the materials necessary to reproduce the paper “Deep Learning for Restoring MPI System Matrices Using Simulated Training Data.” It includes the simulation parameters used to generate a dataset of simulated system matrices, the corresponding noise, the trained models, and the real measurements used for validation. The code is available at: github.com/IBIResearch/mpi-sm-restoration.

Dataset License and Attribution Requirements

This dataset is released under the Creative Commons Attribution 4.0 International License (CC BY 4.0).
Users are free to share, redistribute, and adapt the data for any purpose, provided that appropriate credit is given as required by the license.

To clarify what constitutes appropriate credit for specific parts of this dataset, please include the corresponding citation(s) listed below when your work makes use of data with the indicated name(s).
If you use multiple files or all files from the dataset, please cite all applicable publications.

Name-Specific Attribution Requirements:
1. Files starting with "measurements__snake"
If you use files with this prefix, please cite:
@article{maas_eq_model,
author = {Maass, Marco and Kluth, Tobias and Droigk, Christine and Albers, Hannes and Scheffler, Konrad and Mertins, Alfred and Knopp, Tobias},
title = {Equilibrium Model With Anisotropy for Model-Based Reconstruction in Magnetic Particle Imaging},
year = {2024},
volume = {10},
pages = {1588--1601},
journal = {IEEE Transactions on Computational Imaging},
publisher = {IEEE},
doi = {10.1109/TCI.2024.3490381}
}

2. Files starting with "measurements__resolution"
If you use files with this prefix, please cite:
@article{knopp_openmpi,
title = {OpenMPIData: An initiative for freely accessible magnetic particle imaging data},
author = {Knopp, Tobias and Szwargulski, Patryk and Griese, Florian and Gr{\"a}ser}, Matthias,
journal = {Data in Brief},
volume = {28},
pages = {104971},
year = {2020},
issn = {2352-3409},
doi = {10.1016/j.dib.2019.104971},
}

3. Files starting with "measurements__spiral"
If you use files with this prefix, please cite:
@article{mohn_resotran,
title = {Characterization of the clinically approved MRI tracer resotran for magnetic particle imaging in a comparison study},
author = {Mohn, Fabian and Scheffler, Konrad and Ackers, Justin and Weimer, Agnes and Wegner, Franz and Thieben, Florian and Ahlborg, Mandy and Vogel, Patrick and Graeser, Matthias and Knopp, Tobias},
journal = {Physics in Medicine \& Biology},
volume = {69},
number = {13},
pages = {135014},
publisher = {IOP Publishing},
doi = {10.1088/1361-6560/ad5828},
year = {2024},
}

4. Files starting with "measurements__rectangle"
If you use files with this prefix, please cite:
@ARTICLE{szwargulski_multipatch,
author={Szwargulski, Patryk and M{\"o}ddel, Martin and Gdaniec, Nadine and Knopp, Tobias},
journal={IEEE Transactions on Medical Imaging},
title={Efficient Joint Image Reconstruction of Multi-Patch Data Reusing a Single System Matrix in Magnetic Particle Imaging},
year={2019},
volume={38},
number={4},
pages={932-944},
doi={10.1109/TMI.2018.2875829}
}

5. Any other file
If you use any file other than from 1-4, please cite:
@misc{tsanda_deep_2025,
title = {Deep {Learning} for {Restoring} {MPI} {System} {Matrices} {Using} {Simulated} {Training} {Data}},
url = {http://arxiv.org/abs/2511.23251},
doi = {10.48550/arXiv.2511.23251},
urldate = {2025-12-02},
publisher = {arXiv},
author = {Tsanda, Artyom and Reiss, Sarah and Scheffler, Konrad and Boberg, Marija and Knopp, Tobias},
month = dec,
year = {2025},
}