IBI: Martin Möddel

Dr. rer. nat. Martin Möddel (Hofmann)

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 212
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 56309
E-Mail: martin.moeddel(at)tuhh.de
E-Mail: m.hofmann(at)uke.de
ORCID: https://orcid.org/0000-0002-4737-7863

Research Interests

My research on tomographic imaging is primarily focused on magnetic particle imaging. In this context, I am engaged in the study of a number of problems, including:

Image reconstruction
- Multi-contrast imaging
- Multi-patch imaging
- Artifact reduction
Magnetic field generation and characterisation
Receive path calibration

Curriculum Vitae

Martin Möddel is a postdoctoral researcher in the group of Tobias Knopp for experimental Biomedical Imaging at the University Medical Center Hamburg-Eppendorf and the Hamburg University of Technology. He received his PhD in physics from the Universität Siegen in 2014 on the topic of characterizing quantum correlations: the genuine multiparticle negativity as entanglement monotone. Prior to his PhD, he studied physics at the Universität Leipzig between 2005 and 2011, where he received his Diplom On the costratified Hilbert space structure of a lattice gauge model with semi-simple gauge group.

Journal Publications

[191963]

Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks.

Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel

in: International Journal on Magnetic Particle Imaging. Mar (2025).

Volume: 11. Number: (1 Suppl 1),

on pages: 1-2

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

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Institution:

Type:

DOI: https://doi.org/10.18416/IJMPI.2025.2503009

URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862

ARXIVID:

PMID:

[www] [BibTex]

Note: inproceedings, magneticfield, ml

Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.

[191963]

Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks.

Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel

in: International Journal on Magnetic Particle Imaging. Mar (2025).

Volume: 11. Number: (1 Suppl 1),

on pages: 1-2

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: https://doi.org/10.18416/IJMPI.2025.2503009

URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862

ARXIVID:

PMID:

[www] [BibTex]

Note: inproceedings, magneticfield, ml

Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.

Conference Proceedings

[191963]

Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks.

Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel

in: International Journal on Magnetic Particle Imaging. Mar (2025).

Volume: 11. Number: (1 Suppl 1),

on pages: 1-2

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: https://doi.org/10.18416/IJMPI.2025.2503009

URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862

ARXIVID:

PMID:

[www] [BibTex]

Note: inproceedings, magneticfield, ml

Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.

[191963]

Title: Current-to-Field Prediction for Non-Linear Magnetic Systems via Neural Networks.

Written by: F. Foerger, P. Jürß, M. Boberg, T. Hau, T. Knopp, and M. Möddel

in: International Journal on Magnetic Particle Imaging. Mar (2025).

Volume: 11. Number: (1 Suppl 1),

on pages: 1-2

Chapter:

Editor:

Publisher:

Series:

Address:

Edition:

ISBN:

how published:

Organization:

School:

Institution:

Type:

DOI: https://doi.org/10.18416/IJMPI.2025.2503009

URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/862

ARXIVID:

PMID:

[www] [BibTex]

Note: inproceedings, magneticfield, ml

Abstract: Accurate magnetic field knowledge is crucial for magnetic particle imaging, affecting performance estimation, sequence generation, and reconstruction. Especially for non-linear field generators, such as those with built-in soft iron, conventional field simulations, such as the finite element method, are computationally demanding. We propose the use of neural networks to predict the coefficients of the spherical harmonic expansions of the fields from the input currents, drastically speeding up current-to-field prediction.

Dr. rer. nat. Martin Möddel (Hofmann)

Research Interests

Curriculum Vitae

Journal Publications

Conference Proceedings

TU Hamburg

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