IBI: 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

[191175]

Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.

Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp

in: International Journal on Magnetic Particle Imaging. (2024).

Volume: 10. Number: (1 Suppl 1),

on pages: 1-4

Chapter:

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DOI: https://doi.org/10.18416/IJMPI.2024.2403028

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PMID:

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers (ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[191175]

Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.

Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp

in: International Journal on Magnetic Particle Imaging. (2024).

Volume: 10. 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.2024.2403028

URL:

ARXIVID:

PMID:

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers (ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Conference Proceedings

[191175]

Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.

Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp

in: International Journal on Magnetic Particle Imaging. (2024).

Volume: 10. 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.2024.2403028

URL:

ARXIVID:

PMID:

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers (ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

[191175]

Title: RegularizedLeastSquares.jl: Modality Agnostic Julia Package for Solving Regularized Least Squares Problems.

Written by: N. Hackelberg, M. Grosser, A. Tsanda, F. Mohn, K. Scheffler, M. Möddel, and T. Knopp

in: International Journal on Magnetic Particle Imaging. (2024).

Volume: 10. 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.2024.2403028

URL:

ARXIVID:

PMID:

[BibTex]

Note: inproceedings, reconstruction, opensoftware, generalsoftware

Abstract: Image reconstruction in Magnetic Particle Imaging (MPI) is an ill-posed linear inverse problem. A standard method for solving such a problem is the regularized least squares approach, which uses, a regularization function to reduce the impact of measurement noise in the reconstructed image by leveraging prior knowledge. Various optimization algorithms, including the Kazcmarz method or the Alternating Direction Method of Multipliers (ADMM), and regularization functions, such asl2or Fused Lasso priors have been employed. Therefore, the creation and implementation of cutting-edge image reconstruction techniques necessitate a robust and adaptable optimization framework. In this work, we present the open-source Julia package RegularizedLeastSquares.jl, which provides a large selection of common optimization algorithms and allows flexible inclusion of regularization functions. These features enable the package to achieve state-of-the-art image reconstruction in MPI.

Dr. rer. nat. Martin Möddel (Hofmann)

Research Interests

Curriculum Vitae

Journal Publications

Conference Proceedings

TU Hamburg

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