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Denoising the system matrix with deep neural networks for better MPI reconstructions
Citation Link: https://doi.org/10.15480/882.15007
Publikationstyp
Journal Article
Date Issued
2025
Sprache
English
TORE-DOI
Volume
11
Issue
1
Article Number
2503047
Citation
International Journal on Magnetic Particle Imaging 11 (1): 2503047 (2025)
Publisher DOI
Scopus ID
Publisher
Infinite Science Publishing
Peer Reviewed
true
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.
DDC Class
616: Deseases
006: Special computer methods
519: Applied Mathematics, Probabilities
Publication version
publishedVersion
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