Dr. rer. nat. Martin Möddel (Hofmann)

Universitätsklinikum Hamburg-Eppendorf (UKE)
Sektion für Biomedizinische Bildgebung
Lottestraße 55
2ter Stock, Raum 203
22529 Hamburg

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:       m.hofmann(at)uke.de
E-Mail:       martin.hofmann(at)tuhh.de
ORCID iD: https://orcid.org/0000-0002-4737-7863

Research Interests

My research focus is magnetic particle imaging, where I study a number problems such as:

  • Multi-contrast imaging
  • Image reconstruction
  • Signal processing

Curriculum Vitae

Martin Möddel is a postdoc 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 Characterizing quantum correlations: the genuine multiparticle negativity as entanglement monotone. Prior to his PhD in between 2005-2011 he studied physics at the Universität Leipzig, where he recieved 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, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 Number: 2
on pages: 1-2
Chapter:
Editor:
Publisher:
Series:
Address:
Edition:
ISBN:
how published:
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Institution:
Type:
DOI: 10.18416/IJMPI.2020.2009041
URL: https://journal.iwmpi.org/index.php/iwmpi/article/view/281
ARXIVID:
PMID:

[doi] [www] [BibTex]

Note: inproceedings

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, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 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:

[doi] [www] [BibTex]

Note: inproceedings

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, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 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:

[doi] [www] [BibTex]

Note: inproceedings

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, C. Brandt
in: International Journal on Magnetic Particle Imaging 2020
Volume: 6 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:

[doi] [www] [BibTex]

Note: inproceedings

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.