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

[191982]
Title: Optimization-based motion estimation in HR-pQCT.
Written by: P. Jürß, T. Knopp, B. Busse, F.N. von Brackel, M. Boberg
in: <em>2025 IEEE International Symposium on Biomedical Imaging (ISBI)</em>. (2025).
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on pages: 1-4
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Note: inproceedings, hrpqct

Abstract: The imaging modality high-resolution peripheral quantitative computed tomography (HR-pQCT) enables assessment of bone mineral density and three-dimensional microarchitecture of peripheral limbs. Due to its long scan time, this modality is especially susceptible towards motion of the patient. The architecture and scanning protocol of existing scanners, which acquire only halfscans, make many existing methods of motion compensation inapplicable. In this work, an iterative motion estimation and compensation approach is proposed that is able to significantly reduce the amount of motion artifacts. This is achieved by jointly optimizing the motion parameters and reference image to minimize the data consistency error by exploiting the quasi-convex behavior of the objective functions observed near the ground truth. The proposed method was evaluated on a large collection of simulated sinograms and was able to remove motion artifacts almost completely.