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

[191083]
Title: Exploiting the Fourier Neural Operator for Parameter Identification in MPI.
Written by: M. Grosser, 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:
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DOI: https://doi.org/10.18416/IJMPI.2024.2403004
URL:
ARXIVID:
PMID:

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

[191083]
Title: Exploiting the Fourier Neural Operator for Parameter Identification in MPI.
Written by: M. Grosser, 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.2024.2403004
URL:
ARXIVID:
PMID:

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

Conference Proceedings

[191083]
Title: Exploiting the Fourier Neural Operator for Parameter Identification in MPI.
Written by: M. Grosser, 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.2024.2403004
URL:
ARXIVID:
PMID:

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.

[191083]
Title: Exploiting the Fourier Neural Operator for Parameter Identification in MPI.
Written by: M. Grosser, 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.2024.2403004
URL:
ARXIVID:
PMID:

[BibTex]

Note: inproceedings

Abstract: Model-based magnetic particle imaging (MPI) is a challenging task both due to the complicated underlying physical model and the high numerical effort required for the solution of the corresponding equations of motion. A second challenge for practical applications is the identification of model parameters that are consistent with the given experimental setting and produce accurate predictions of the MPI signals. In this work, we show how the parameter identification problem can be addressed using a learned physics simulator based on the Fourier neural operator. As an application, we show how model-based system matrices can be estimated from a small set of calibration measurements, which can also be interpreted as a model-based approach to system matrix recovery. We compared our approach to established compressed sensing and interpolation schemes and found that it outperformed both.