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

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging.
Written by: L. Nawwas, M. Möddel, T. Knopp, and C. Brandt
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020).
Volume: <strong>6</strong>. Number: (2),
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging.
Written by: L. Nawwas, M. Möddel, T. Knopp, and C. Brandt
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020).
Volume: <strong>6</strong>. Number: (2),
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.

Conference Proceedings

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging.
Written by: L. Nawwas, M. Möddel, T. Knopp, and C. Brandt
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020).
Volume: <strong>6</strong>. Number: (2),
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.

[146886]
Title: Bias-reduction for sparsity promoting regularization in Magnetic Particle Imaging.
Written by: L. Nawwas, M. Möddel, T. Knopp, and C. Brandt
in: <em>International Journal on Magnetic Particle Imaging</em>. (2020).
Volume: <strong>6</strong>. Number: (2),
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
Organization:
School:
Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[www] [BibTex]

Note: inproceedings, artifact

Abstract: Magnetic Particle Imaging (MPI) is a tracer based medical imaging modality with great potential due to its high sensitivity, high spatial and temporal resolution, and ability to quantify the tracer concentration. Image reconstruction in MPI is an ill-posed problem that can be addressed by regularization methods that each lead to a bias. Reconstruction bias in MPI is most apparent in a mismatch between true and reconstructed tracer distribution. This is expressed globally in the spatial support of the distribution and locally in its intensity values. In this work, MPI reconstruction bias and its impact are investigated and a two-step debiasing method with significant bias reduction capabilities is introduced.