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IBI > People > 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

[191961]
Title: Neural implicit representations for grid-agnostic MPI reconstructions.
Written by: A. Tsanda, S. Khalid, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages:
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2025.2503058
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, ml

Abstract: Magnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system. This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the two-stage Kaczmarz-$\ell_2$ reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.

[191961]
Title: Neural implicit representations for grid-agnostic MPI reconstructions.
Written by: A. Tsanda, S. Khalid, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages:
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2025.2503058
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, ml

Abstract: Magnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system. This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the two-stage Kaczmarz-$\ell_2$ reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.

Conference Proceedings

[191961]
Title: Neural implicit representations for grid-agnostic MPI reconstructions.
Written by: A. Tsanda, S. Khalid, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages:
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2025.2503058
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, ml

Abstract: Magnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system. This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the two-stage Kaczmarz-$\ell_2$ reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.

[191961]
Title: Neural implicit representations for grid-agnostic MPI reconstructions.
Written by: A. Tsanda, S. Khalid, M. Möddel, and T. Knopp
in: <em>International Journal on Magnetic Particle Imaging</em>. Mar (2025).
Volume: <strong>11</strong>. Number: (1 Suppl 1),
on pages:
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2025.2503058
URL: https://www.journal.iwmpi.org/index.php/iwmpi/article/view/813
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, ml

Abstract: Magnetic particle imaging (MPI) reconstructs the spatial distribution of magnetic nanoparticles on a fixed grid, the resolution of which is limited by the noise present in the system. This paper addresses the reconstruction problem while integrating single-image super-resolution for concentration maps. We introduce Neural Implicit Representations (NIR) as an image prior, enabling arbitrary grid size sampling after training. Experimental results using a spiral phantom measurement reveal that NIR-based reconstruction maintains image sharpness across diverse grid sizes, surpassing the two-stage Kaczmarz-$\ell_2$ reconstruction followed by bicubic up-sampling in preserving fine structural details. This technique has a potential for high-resolution MPI imaging without relying on extensive datasets.

TU Hamburg
Institute for Biomedical Imaging
Lottestraße 55, 2nd floor
22529 Hamburg
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