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Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer
Citation Link: https://doi.org/10.15480/882.9511
Publikationstyp
Journal Article
Publikationsdatum
2024
Sprache
English
Volume
10
Issue
1, suppl. 1
Article Number
2403010
Citation
International Journal on Magnetic Particle Imaging 10 (1, suppl. 1): 2403010 (2024)
Publisher DOI
Scopus ID
Publisher
Infinite Science Publishing
The Kaczmarz algorithm is widely used for image reconstruction in magnetic particle imaging (MPI) because it converges rapidly and 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.
DDC Class
610: Medicine, Health
620: Engineering
Publication version
publishedVersion
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IJMPI-Vol10-Iss1Suppl1-748.pdf
Type
main article
Size
459.02 KB
Format
Adobe PDF