Paul Jürß, M.Sc.

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 210
22529 Hamburg

Technische Universität Hamburg (TUHH)
Institut für Biomedizinische Bildgebung
Gebäude E, Raum 4.044
Am Schwarzenberg-Campus 3
21073 Hamburg

Tel.: 040 / 7410 25811
E-Mail: paul.juerss(at)
E-Mail: p.juerss(at)

profile picture of Paul Jürß

Research Interests

  • Image Reconstruction
  • Machine Learning

Curriculum Vitae

Paul Jürß is a PhD student in the group of Tobias Knopp for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology. In 2020, he graduated with a bachelor's degree in Computer Science in Engineering at the Hamburg University of Technology. From 2020 to 2022, he studied Technomathematics at the University of Hamburg and obtained his master's degree with a thesis on "Compensation of motion artifacts in HR-pQCT".

Conference Proceedings

Title: Extension of the Kaczmarz algorithm with a deep plug-and-play regularizer.
Written by: A. Tsanda, P. Jürß, N. Hackelberg, M. Grosser, M. Möddel, T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2023).
Volume: <strong>10</strong>. Number: (1),
on pages:
how published:
DOI: 10.18416/IJMPI.2024.2403010


Note: inproceedings, online reconstruction

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.