@article{TsandaKaczmarzPnP2024IWMPI,
author = {A. Tsanda, P. Jürß, N. Hackelberg, M. Grosser, M. Möddel, and T. Knopp},
title = {Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer.},
journal = {International Journal on Magnetic Particle Imaging.},
year = {2024},
volume = {10.},
number = {(1 Suppl 1),},
pages = {1-4},
month = {Mar},
note = {inproceedings, online reconstruction},
doi = {10.18416/IJMPI.2024.2403010},
url = {https://www.journal.iwmpi.org/index.php/iwmpi/article/view/748},
keywords = {inproceedings},
abstract = {The Kaczmarz algorithm is widely used for image reconstruction in magnetic particle imaging (MPI) because it converges rapidly and often provides good image quality even after a few iterations. It is often combined with Tikhonov regularization to cope with noisy measurements and the ill-posed nature of the imaging problem. In this abstract, we propose to combine the Kaczmarz method with a plug-and-play (PnP) denoiser for regularization, which can provide more specific prior knowledge than handcrafted priors. Using measurement data of a spiral phantom, we show that Kaczmarz-PnP yields excellent image quality, while speeding up the already fast convergence. Since the PnP denoiser is not coupled to the imaging operator, the Kaczmarz-PnP method is very generic and can be used for image reconstruction independently of the measurement sequence and MPI tracer type.}
}

@COMMENT{Bibtex file generated on 2026-6-30 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/publications }