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Title: Neural implicit representations for grid-agnostic MPI reconstructions. |
Written by: A. Tsanda, S. Khalid, M. Möddel, and T. Knopp |
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025). |
Volume: <strong>11</strong>. Number: (1 Suppl 1), |
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DOI: 10.18416/IJMPI.2025.2503058 |
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813 |
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Note: inproceedings, ml
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.