Dr. Tobias Kluth

Projects

1. DFG-Graduiertenkolleg: π³ Parameter Identification – Analysis, Algorithms, Applications (01.10.2016 - 31.03.2021)
2. DFG - Bimodal reconstruktion and Magnetic Particle Imaging (since 01.08.2015)

Leader of Projects

1. Dynamic Inverse Problems in Magnetic Particle Imaging (D-MPI) (01.05.2020 - 30.04.2023)
2. DELETO - Machine learning with correlative MR and high-throughput NanoCT (01.04.2020 - 31.03.2023)
3. BMBF-MPI²: Model-based parameter identification in magnetic particle imaging (01.12.2016 - 30.11.2019)
4. Magnetic Particle Imaging (since 01.03.2016)

Courses (Selection)complete list

1. Modeling Project (Part 1) (Sommersemester 2024)
2. Mathematik für Maschinenbau und Verfahrenstechnik (Wintersemester 2023/2024)
3. Advanced Topics in Inverse Problems (Sommersemester 2023)
4. Deep Learning for Inverse Problems (Sommersemester 2023)
5. Nonlinear Inverse Problems (Sommersemester 2023)

Theses (Selection)complete list

1. A different approach of the Deep Image Prior on CT-Imaging (Pegah Golchian)
2. Parabolische partielle Differentialgleichungen auf Mannigfaltigkeiten (Alexander West)
3. Modellunsicherheiten im Magnetic Particle Imaging – Rekonstruktion mittels Kleinste-Quadrate-Methode (Mahir Gürsoy)
4. Joint-Motion und Bildrekonstruktion für Magnetic Particle Imaging in 2D und 3D (Dennis Zvegincev)
5. Deep Learning in der Anwendung des Magnetic Particle Imaging (Johannes Leuschner)

Publications (Selection)complete list

1. H. Albers, T. Kluth.
   Time-dependent parameter identification in a Fokker-Planck equation based magnetization model of large ensembles of nanoparticles.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2307.03560

2. C. Brandt, T. Kluth, T. Knopp, L. Westen.
   Dynamic image reconstruction with motion priors in application to 3d magnetic particle imaging.
   Zur Veröffentlichung eingereicht.

   online at: https://arxiv.org/abs/2306.11625

3. T. Lütjen, F. Schönfeld, J. Leuschner, M. Schmidt, A. Wald, T. Kluth.
   Learning-based approaches for reconstructions with inexact operators in nanoCTapplications.
   Zur Veröffentlichung eingereicht.

   online at: https://aps.arxiv.org/abs/2307.10474

4. C. Arndt, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, J. Nickel.
   Bayesian view on the training of invertible residual networks for solving linear inverse problems.
   Zur Veröffentlichung eingereicht.

   online at: https://www.x-mol.net/paper/article/1682514725633245184

5. C. Arndt, A. Denker, S. Dittmer, N. Heilenkötter, M. Iske, T. Kluth, P. Maaß, J. Nickel.
   Invertible residual networks in the context of regularization theory for linear inverse problems.
   Inverse Problems, 39(12), IOPscience, 2023.

   DOI: 10.1088/1361-6420/ad0660
   online at: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660