@article{TsandaSMDenoising2025IWMPI,
Author = {A. Tsanda, K. Scheffler, S. Reiss, and T. Knopp},
Title = {Denoising the system matrix with deep neural networks for better MPI reconstructions.},
Journal = {<em>International Journal on Magnetic Particle Imaging</em>.},
Year = {(2025).},
Volume = {<strong>11</strong>.},
Number = {(1 Suppl 1),},
Month = {Mar},
Note = {inproceedings, ml},
Doi = {10.18416/IJMPI.2025.2503047},
Url = {https://www.journal.iwmpi.org/index.php/iwmpi/article/view/810},
Keywords = {inproceedings},
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.}
}

@article{Reiss2025ModelBasedIWMPI,
Author = {S. Reiss, F. Thieben, J. Faltinath, T. Knopp, M. Boberg},
Title = {Model-Based Reconstruction in MPI accounting for Field Imperfections.},
Journal = {<em>International Journal on Magnetic Particle Imaging</em>.},
Year = {(2025).},
Volume = {<strong>11</strong>.},
Number = {(1 Suppl 1),},
Pages = {1-2},
Note = {inproceedings, model-based},
Doi = {https://doi.org/10.18416/IJMPI.2025.2503039},
Url = {https://www.journal.iwmpi.org/index.php/iwmpi/article/view/806},
Keywords = {inproceedings, reconstruction, multi-patch},
Abstract = {To date a system matrix has to be obtained through a tedious calibration measurement when employing a systemmatrix-based reconstruction in magnetic particle imaging. This problem can be effectively addressed by model-based reconstruction, which takes into account both particle and scanner parameters. In this study, we focus onthe scanner parameters and in particular on the fact that the fields of experimental systems are imperfect.  Forexperimental Lissajous-type data we show that the modeling error can be substantially reduced by about 18 % by incorporating field imperfections in both the transmit and receive coils.}
}

@COMMENT{Bibtex file generated on 2026-5-13 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/people/sarah-reiss }