@article{TsandaNIR4MPI2025IWMPI,
author = {A. Tsanda, S. Khalid, M. Möddel, and T. Knopp},
title = {Neural implicit representations for grid-agnostic MPI reconstructions.},
journal = {International Journal on Magnetic Particle Imaging.},
year = {2025},
volume = {11.},
number = {(1 Suppl 1),},
month = {Mar},
note = {inproceedings, ml},
doi = {10.18416/IJMPI.2025.2503058},
url = {https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813},
keywords = {inproceedings},
abstract = {Magnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system.
This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. 
We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the  two-stage Kaczmarz-$\ell_2$ reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.}
}

@COMMENT{Bibtex file generated on 2026-6-28 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/publications }