Konrad Scheffler, M.Sc.

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 213
22529 Hamburg
- Postanschrift -

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

Tel.: 040 / 7410 25813
E-Mail: konrad.scheffler(at)tuhh.de
E-Mail: ko.scheffler(at)uke.de

Research Interests

  • Magnetic Particle Imaging
  • Image Reconstruction

Curriculum Vitae

Konrad Scheffler 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. He studied Technomathematics between 2015 and 2021 in Hamburg and graduated with a master's degree thesis on "Enhancing matrix compression using convoluted tensor products of Chebyshev polynomials".

Journal Publications

[164758]
Title: Sparsifying system matrices by combined usage of compressed sensing and extrapolation.
Written by: K. Scheffler, M. Grosser, M. Boberg, and T. Knopp
in: <em>12th International Workshop on Magnetic Particle Imaging (IWMPI 2023)</em>. (2023).
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URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/493
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Note: inproceedings

Abstract: In magnetic particle imaging the calibration step for a system matrix based reconstruction is very time and memory consuming. System matrices need to be measured not only in the physical field of view, but also in a bigger overscan region to avoid artifacts, especially in the case of multi-patch magnetic particle imaging. There are several methods to reduce the total number of voxels that need to be measured, e.g. compressed sensing and system matrix extrapolation. In this work, we show that a combination of these two methods is possible by using compressed sensing on a sparse sampling pattern only in the field of view and extrapolating the signal in the overscan region afterwards. We demonstrate on measured data, that such a combination gives superior results than using only compressed sensing on the whole system matrix. This is clearly manifested in the reduction of noise in the reconstruction result, especially when using a high undersampling factor.

Conference Publications

[164758]
Title: Sparsifying system matrices by combined usage of compressed sensing and extrapolation.
Written by: K. Scheffler, M. Grosser, M. Boberg, and T. Knopp
in: <em>12th International Workshop on Magnetic Particle Imaging (IWMPI 2023)</em>. (2023).
Volume: Number:
on pages: 1-1
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI:
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/493
ARXIVID:
PMID:

[www]

Note: inproceedings

Abstract: In magnetic particle imaging the calibration step for a system matrix based reconstruction is very time and memory consuming. System matrices need to be measured not only in the physical field of view, but also in a bigger overscan region to avoid artifacts, especially in the case of multi-patch magnetic particle imaging. There are several methods to reduce the total number of voxels that need to be measured, e.g. compressed sensing and system matrix extrapolation. In this work, we show that a combination of these two methods is possible by using compressed sensing on a sparse sampling pattern only in the field of view and extrapolating the signal in the overscan region afterwards. We demonstrate on measured data, that such a combination gives superior results than using only compressed sensing on the whole system matrix. This is clearly manifested in the reduction of noise in the reconstruction result, especially when using a high undersampling factor.