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

[56725]
Title: Efficient Joint Image Reconstruction of Multi-Patch Data reusing a Single System Matrix in Magnetic Particle Imaging.
Written by: P. Szwargulski, M. Möddel, N. Gdaniec and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. April (2019).
Volume: <strong>38</strong>. Number: (4),
on pages: 932-944
Chapter:
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how published:
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DOI: 10.1109/TMI.2018.2875829
URL: https://ieeexplore.ieee.org/document/8490900
ARXIVID:
PMID:

[www] [BibTex]

Note: article, multi-patch

Abstract: Due to peripheral nerve stimulation the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger field of view, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this work, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3D multi-patch phantom datasets and shown to reconstruct a large datasets with 15 patches in less than 22 seconds.

[56725]
Title: Efficient Joint Image Reconstruction of Multi-Patch Data reusing a Single System Matrix in Magnetic Particle Imaging.
Written by: P. Szwargulski, M. Möddel, N. Gdaniec and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. April (2019).
Volume: <strong>38</strong>. Number: (4),
on pages: 932-944
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1109/TMI.2018.2875829
URL: https://ieeexplore.ieee.org/document/8490900
ARXIVID:
PMID:

[www] [BibTex]

Note: article, multi-patch

Abstract: Due to peripheral nerve stimulation the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger field of view, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this work, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3D multi-patch phantom datasets and shown to reconstruct a large datasets with 15 patches in less than 22 seconds.

Conference Proceedings

[56725]
Title: Efficient Joint Image Reconstruction of Multi-Patch Data reusing a Single System Matrix in Magnetic Particle Imaging.
Written by: P. Szwargulski, M. Möddel, N. Gdaniec and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. April (2019).
Volume: <strong>38</strong>. Number: (4),
on pages: 932-944
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1109/TMI.2018.2875829
URL: https://ieeexplore.ieee.org/document/8490900
ARXIVID:
PMID:

[www] [BibTex]

Note: article, multi-patch

Abstract: Due to peripheral nerve stimulation the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger field of view, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this work, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3D multi-patch phantom datasets and shown to reconstruct a large datasets with 15 patches in less than 22 seconds.

[56725]
Title: Efficient Joint Image Reconstruction of Multi-Patch Data reusing a Single System Matrix in Magnetic Particle Imaging.
Written by: P. Szwargulski, M. Möddel, N. Gdaniec and T. Knopp
in: <em>IEEE Transactions on Medical Imaging</em>. April (2019).
Volume: <strong>38</strong>. Number: (4),
on pages: 932-944
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.1109/TMI.2018.2875829
URL: https://ieeexplore.ieee.org/document/8490900
ARXIVID:
PMID:

[www] [BibTex]

Note: article, multi-patch

Abstract: Due to peripheral nerve stimulation the magnetic particle imaging (MPI) method is limited in the maximum applicable excitation-field amplitude. This in turn leads to a limitation of the size of the covered field of view (FoV) to few millimeters. In order to still capture a larger field of view, MPI is capable to rapidly acquire volumes in a multi-patch fashion. To this end, the small excitation volume is shifted through space using the magnetic focus fields. Recently it has been shown that the individual patches are preferably reconstructed in a joint fashion by solving a single linear system of equations taking the coupling between individual patches into account. While this improves the image quality, it is computationally and memory demanding since the size of the linear system increases in the best case quadratically with the number of patches. In this work, we will develop a reconstruction algorithm for MPI multi-patch data exploiting the sparsity of the joint system matrix. A highly efficient implicit matrix format allows for rapid on-the-fly calculations of linear algebra operations involving the system matrix. Using this approach the computational effort can be reduced to a linear dependence on the number of used patches. The algorithm is validated on 3D multi-patch phantom datasets and shown to reconstruct a large datasets with 15 patches in less than 22 seconds.