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)tuhh.de
E-Mail: p.juerss(at)uke.de
ORCID: https://orcid.org/0000-0002-3475-8480

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

[191958]
Title: Power-Efficient Control of Non-LinearMagnetic Field Generators for MPI.
Written by: P. Suskin, F. Foerger, P. Jürß, M. Boberg, T. Knopp, and M. Möddel
in: <em>International Journal on Magnetic Particle Imaging</em>. (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages: 1-2
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DOI: https://doi.org/10.18416/IJMPI.2025.2503023
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/903
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Note: inproceedings, magneticfield, ml

Abstract: The scaling of electrical power constitutes a significant challenge when adapting Magnetic Particle Imaging (MPI) to a human scale. The use of coils incorporating soft-iron cores serves to reduce power usage, but also introduces spatial imperfections and non-linearities in the current-to-field relationship. This study proposes methodologies for the control of the magnetic field output of a system comprising 18 coils, subject to the influence of saturated iron. In particular, we integrate current sequence optimization with neural network-based predictions for field and gradient values, thereby enabling the precise and power-optimal generation of magnetic fields. The proposed framework for controlling non-linear magnetic field generators represents a significant advancement in MPI technology, paving the way for the development of human-scale, power-efficient medical imaging solutions.