Dr.-Ing. Fabian Mohn

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 203
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 25812
E-Mail: f.mohn(at)uke.de
E-Mail: fabian.mohn(at)tuhh.de
ORCID:  https://orcid.org/0000-0002-9151-9929

Research Interests

  • (arbitrary waveform) Magnetic Particle Imaging
  • inductive sensors, filters and resonant transformers
  • circuit design, impedance matching
  • Magneto Mechanical Resonators (MMRs)

Curriculum Vitae

Fabian Mohn studied Electrical Engineering at the Hamburg University of Technology (TUHH) and in cooperation with the Philips Research Laboratories Hamburg, he received his master's degree in 2018 on the Analysis and Optimization of the Signal-to-Noise Ratio for Receive Arrays in Magnetic Resonance Imaging. He joined the group of Tobias Knopp for Biomedical Imaging at the University Medical Center Hamburg-Eppendorf (UKE) and the Hamburg University of Technology in 2020 as a PhD student and finished his PhD in 2024 on the topic Instrumentation, Sequences and Applications for Magnetic Particles in Imaging and Spectroscopy.

Journal Publications

[191175]
Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.
Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2024).
Volume: <strong>10</strong>. Number: (1 Suppl 1),
on pages: 1-4
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DOI: https://doi.org/10.18416/IJMPI.2024.2403028
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Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Conference Proceedings

[191175]
Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.
Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. (2024).
Volume: <strong>10</strong>. Number: (1 Suppl 1),
on pages: 1-4
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: https://doi.org/10.18416/IJMPI.2024.2403028
URL:
ARXIVID:
PMID:

[BibTex]

Note: inproceedings, reconstruction

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard methodfor solving such a problem is the regularized least squares approach, which uses, a regularization function toreduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Variousoptimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers(ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, thecreation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptableoptimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, whichprovides a large selection of common optimization algorithms and allows flexible inclusion of regularizationfunctions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.