Tsanda, ArtyomArtyomTsandaScheffler, KonradKonradSchefflerReiß, SarahSarahReißKnopp, TobiasTobiasKnopp2025-04-042025-04-042025International Journal on Magnetic Particle Imaging 11 (1): 2503047 (2025)https://hdl.handle.net/11420/55183Magnetic 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.en2365-903320251Infinite Science Publishinghttps://creativecommons.org/licenses/by/4.0/Technology::616: DeseasesComputer Science, Information and General Works::006: Special computer methodsNatural Sciences and Mathematics::519: Applied Mathematics, ProbabilitiesJournal Articlehttps://doi.org/10.15480/882.1500710.18416/IJMPI.2025.250304710.15480/882.15007Journal Article