Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer

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

Funding(s)

SFB 1615 - SMARTe Reaktoren für die Verfahrenstechnik der Zukunft

SFB 1615 - Teilprojekt B03: Magnetresonanzbildgebung von großräumigen mehrphasigen und reaktiven Strömungssystemen

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publishedVersion

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https://creativecommons.org/licenses/by/4.0/

Name

IJMPI-Vol10-Iss1Suppl1-748.pdf

Type

Main Article

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459.02 KB

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Adobe PDF

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Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer