International Journal on Magnetic Particle Imaging IJMPI
Vol. 11 No. 1 Suppl 1 (2025): Int J Mag Part Imag
https://doi.org/10.18416/IJMPI.2025.2503047

Proceedings Articles

Denoising the system matrix with deep neural networks for better MPI reconstructions

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Artyom Tsanda (Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany), Konrad Scheffler (1) Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 2) Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany), Sarah Reiss (1) Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 2)Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany), Tobias Knopp (1) Section for Biomedical Imaging, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; 2) Institute for Biomedical Imaging, Hamburg University of Technology, Hamburg, Germany; 3) Fraunhofer Research Institution for Individualized and Cell-based Medical Engineering IMTE, Lübeck, Germany)

Copyright (c) 2025 Artyom Tsanda, Konrad Scheffler, Sarah Reiss, Tobias Knopp

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Magnetic Particle Imaging commonly relies on the system matrix (SM) to reconstruct particle distributions, but noise during acquisition limits both its resolution and image quality. Traditionally, noise reduction requires averaging multiple measurements, which increases acquisition time. This paper presents a deep neural network trained on simulated SMs and measured background noise, which effectively generalizes to real-world data. The model recovers higher frequency components of the SM and serves as a general pre-processing step, enhancing image reconstruction quality while reducing the need for extensive averaging, thus accelerating SM acquisition.

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