@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 = {<em>International Journal on Magnetic Particle Imaging</em>.},
Year = {(2024).},
Volume = {<strong>10</strong>.},
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 2026-5-13 with typo3 si_bibtex plugin. Data from https://www.tuhh.de/ibi/people/martin-moeddel }