Dr. Tobias Kluth

Dr. Tobias Kluth

Wissenschaftlicher Mitarbeiter

Inverse Probleme und Magnetic Particle Imaging

Bibliothekstraße 5
28359 Bremen

Raum: MZH 2090
Telefon: +49 421 218-63817
E-Mail: tkluthprotect me ?!math.uni-bremenprotect me ?!.de

Forschungsgebiete

  • Inverse Probleme
  • Parameteridentifikation
  • Mathematische Modellierung und Simulation
  • Deep Learning
  • Wissenschaftliches Rechnen

Projekte

  • DELETO - Maschinelles Lernen bei korrelativer MR und Hochdurchsatz-NanoCT
  • D-MPI - Dynamische Inverse Probleme in Magnetic Particle Imaging
  • Graduiertenkolleg π³ - Parameter Identification – Analysis, Algorithms, Applications
  • MPI² - Modellbasierte Parameteridentifikation in Magnetic Particle Imaging


Zeitschriftenartikel

H. Albers, T. Knopp, M. Möddel, M. Boberg, T. Kluth.
Modeling the magnetization dynamics for large ensembles of immobilized magnetic nanoparticles in multi-dimensional magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 543, 168534, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168534

H. Albers, T. Kluth, T. Knopp.
Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
Journal of Magnetism and Magnetic Materials, 541, 168508, Elsevier, 2022.
DOI: 10.1016/j.jmmm.2021.168508
online unter: https://www.sciencedirect.com/science/article/abs/pii/S0304885321007678

S. Dittmer, T. Kluth, M. Henriksen, P. Maaß.
Deep image prior for 3D magnetic particle imaging: A quantitative comparison of regularization techniques on Open MPI dataset.
International Journal on Magnetic Particle Imaging, 7(1), 2021.
online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/148

M. Möddel, F. Griese, T. Kluth, T. Knopp.
Estimating the Spatial Orientation of Immobilized Magnetic Nanoparticles with Parallel-Aligned Easy Axes.
Physical Review Applied, 16(4), L041003 S., 2021.

T. Kluth.
Recent developments on system function/matrix representation, hybrid simulation techniques, and magnetic actuation.
International Journal on Magnetic Particle Imaging, 6(1), 2020.
DOI: https://journal.iwmpi.org/index.php/iwmpi/article/view/327

T. Kluth, H. Albers.
Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
Erscheint in Oberwolfach Reports
DOI: 10.1088/1361-6420/aad015

T. Kluth, C. Bathke, M. Jiang, P. Maaß.
Joint super-resolution image reconstruction and parameter identification in imaging operator: Analysis of bilinear operator equations, numerical solution, and application to magnetic particle imaging.
Erscheint in Inverse Problems
online unter: https://arxiv.org/abs/2004.13091

T. Kluth, B. Jin.
L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
Erscheint in International Journal on Magnetic Particle Imaging
online unter: https://arxiv.org/abs/2001.06083

S. Dittmer, T. Kluth, P. Maaß, D. Otero Baguer.
Regularization by architecture: A deep prior approach for inverse problems.
Journal of Mathematical Imaging and Vision, :456-470, Springer Verlag, 2020.
DOI: 10.1007/s10851-019-00923-x
online unter: http://link.springer.com/article/10.1007/s10851-019-00923-x

J. Clemens, T. Kluth, T. Reineking.
β - SLAM: Simultaneous Localization an Grid Mapping with Beta Distributions.
Information Fusion, 52:62-75, Elsevier, 2019.
DOI: 10.1016/j.inffus.2018.11.005

T. Kluth, B. Jin.
Enhanced Reconstruction in Magnetic Particle Imaging by Whitening and Randomized SVD Approximation.
Physics in Medicine and Biology, Article ID 125026 64(12), 2019.
DOI: 10.1088/1361-6560/ab1a4f

T. Kluth, P. Szwargulski, T. Knopp.
Towards Accurate Modeling of the Multidimensional Magnetic Particle Imaging Physics.
New Journal of Physics, Article ID 10303 21, 10 pp., 2019.
online unter: https://iopscience.iop.org/article/10.1088/1367-2630/ab4938/pdf

T. Kluth.
Mathematical models for magnetic particle imaging.
Inverse Problems, Article ID 083001 34(8), 2018.
DOI: 10.1088/1361-6420/aac535

T. Kluth, B. Jin, G. Li.
On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
Inverse Problems, Article ID 095006 34(9), 2018.
DOI: 10.1088/1361-6420/aad015

C. Bathke, T. Kluth, C. Brandt, P. Maaß.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Journal on Magnetic Particle Imaging, Article ID 1703015, 3(1), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1703015

T. Kluth, P. Maaß.
Model uncertainty in magnetic particle imaging: Nonlinear problem formulation and model-based sparse reconstruction.
International Journal on Magnetic Particle Imaging, Article ID 1707004 3(2), 10 pages, 2017.
DOI: 10.18416/ijmpi.2017.1707004

J. Clemens, T. Reineking, T. Kluth.
An evidential approach to SLAM, path planning, and active exploration.
International Journal of Approximate Reasoning, 73:1-26, 2016.

T. Kluth, C. Zetzsche.
Numerosity as a topological invariant.
Journal of Vision, 16(3):1-39, 2016.

M. Gehre, T. Kluth, C. Sebu, P. Maaß.
Sparse 3D reconstructions in electrical impedance tomography using real data.
Inverse Problems in Science and Engineering, 22(1):31-44, Taylor & Francis, 2014.
DOI: 10.1080/17415977.2013.827183

M. Gehre, T. Kluth, A. Lipponen, B. Jin, A. Seppänen, J. P. Kaipio, P. Maaß.
Sparsity Reconstruction in Electrical Impedance Tomography: An Experimental Evaluation.
Journal of Computational and Applied Mathematics, 236(8):2126-2136, 2012.
DOI: 10.1016/j.cam.2011.09.035


Preprints

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 unter: https://arxiv.org/abs/2307.03560

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.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2306.01335

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

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 unter: https://arxiv.org/abs/2306.11625

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 unter: https://aps.arxiv.org/abs/2307.10474

T. Kluth, B. Jin.
Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging.
Zur Veröffentlichung eingereicht.

 

Tagungsbeiträge

H. Albers, T. Kluth.
Immobilized nanoparticles with uniaxial anisotropy in multi-dimensional lissajous-type excitation: An equilibrium model approach.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203048

M. Nitzsche, H. Albers, T. Kluth, B. Hahn.
Compensating model imperfections during image reconstruction via resesop.
International Workshop on Magnetic Particle Imaging, 21.03.-23.03.2022, University of Würzburg, Deutschland.
International Journal on Magnetic Particle Imaging, 8(1):4 pages, 2022.
DOI: 10.18416/IJMPI.2022.2203062

S. Dittmer, T. Kluth, D. Otero Baguer, B. Maass.
A Deep Prior Approach to Magnetic Particle Imaging.
Machine Learning for Medical Image Reconstruction 2020.
Springer International Publishing, F. Deeba, P. Johnson, T. Würfl, J. C. Ye (Hrsg.), S. 113-122, 2020.

M. Möddel, F. Griese, T. Kluth, T. Knopp.
Estimating orientation using multi-contrast MPI.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.

H. Albers, T. Kluth, T. Knopp.
MNPDynamics: A computational toolbox for simulating magnetic moment behavior of ensembles of nanoparticles.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.

T. Kluth, P. Szwargulski, T. Knopp.
Towards accurate modeling of the multidimensional MPI physics.
10th International Workshop on Magnetic Particle Imaging 2020, Würzburg, 07.09.-09.09.2020.
Erscheint in Infinite Science Publishing, T. Knopp, T. M. Buzug (Hrsg.), S. 2 pages.

T. Kluth, B. Hahn, C. Brandt.
Spatio-temporal concentration reconstruction using motion priors in magnetic particle imaging.
International Workshop on Magnetic Particle Imaging 2019.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2019, S. 23-24, Infinite Science Publishing, 2019.

T. Kluth, B. Jin.
Exploiting Ill-Posedness in Magnetic Particle Imaging - System Matrix Approximation via Randomized SVD.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 127-128, Infinite Science Publishing, 2018.

J. Flötotto, T. Kluth, M. Möddel, T. Knopp, P. Maaß.
Improving Generalization Properties of Measured System Matrices by Using Regularized Total Least Squares Reconstruction in MPI.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 53-54, Infinite Science Publishing, 2018.

C. Bathke, T. Kluth, P. Maaß.
MPI Reconstruction Using Structural Prior Information and Sparsity.
International Workshop on Magnetic Particle Imaging 2018.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2018, S. 129-130, Infinite Science Publishing, 2018.

C. Bathke, T. Kluth, C. Brandt, P. Maaß.
Improved image reconstruction in magnetic particle imaging using structural a priori information.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, S. 85, Infinite Science Publishing, 2017.

T. Kluth, P. Maaß.
Model uncertainty in magnetic particle iamging: Motivating nonlinear problems by model-based sparse reconstruction.
International Workshop on Magnetic Particle Imaging 2017.
International Workshop on Magnetic Particle Imaging (IWMPI) Book of Abstracts 2017, S. 83, Infinite Science Publishing, 2017.

T. Reineking, T. Kluth, D. Nakath.
Adaptive information selection in images: Efficient naive bayes nearest neighbor classification.
16th International Conference on Computer Analysis of Images and Patterns, Valetta, Malta, 02.09.-04.09.2015.
Lecture Notes in Computer Science, Proceedings CAIP, 9256:350-361, Springer Verlag, 2015.
DOI: 10.1007/978-3-319-23192-1_29

D. Nakath, T. Kluth, T. Reineking, C. Zetzsche, K. Schill.
Active sensorimotor object recognition in three-dimensional space.
Spatial Cognition IX, 2014.
Lecture Notes in Computer Science, volume 6684, Spatial Cognition IX, S. 312-324, Springer Verlag, 2014.

T. Kluth, D. Nakath, T. Reineking, C. Zetzsche, K. Schill.
Affordance-based object recognition using interactions obtained from a utility maximization principle.
European Conference on Computer Vision, 2014.
Lecture Notes in Computer Science, volume 8926, Computer Vision-ECCV 2014 Workshops, S. 406-412, Springer Verlag, 2014.

T. Kluth, C. Zetzsche.
Spatial numerosity: A computational model based on a topological invariant.
Spatial Cognition IX, 2014.
Lecture Notes in Computer Science, volume 6684, Spatial Cognition IX, S. 237-252, Springer Verlag, 2014.

C. Zetzsche, K. Gadzicki, T. Kluth.
Statistical Invariants of Spatial Form: From Local AND to Numerosity.
Second Interdisciplinary Workshop The Shape of Things, 2013.
Proceedings of the Second Interdisciplinary Workshop The Shape of Things, S. 163-172, 2013.

S. Eberhardt, T. Kluth, C. Zetzsche, K. Schill.
From Pattern Recognition to Place Detection.
International Workshop on Place-related Knowledge Acquisition Research (P-KAR), 2012.
Proceedings of the International Workshop on Place-related Knowledge Acquisition Research (P-KAR), S. 39-44, 2012.

2011Diplom Technomathematik, Universität Bremen, "3D Electrical Impedance Tomography with Sparsity Constraints."
2011 - 2015Doktorand, Kognitive Informatik, Universität Bremen
2015Promotion zum Dr.-Ing., Universität Bremen, "Intrinsic dimensionality in vision: Nonlinear filter design and applications."
Betreuer: Dr. Christoph Zetzsche.
seit 2016Postdoc am Zentrum für Technomathematik, Universität Bremen
2021Habilitation in Mathematics/Industrial Mathematics, University of Bremen, "Model-based to data-driven approaches for parameter identification and image reconstruction in the applied inverse problem of magnetic particle imaging."
  • Vorlesung, Nicht-lineare inverse Probleme: Analysis, Anwendungen und Algorithmen, 03-M-WP-47, WiSe 2020/2021
  • Kurs, BrückenMathematik, 03-M-BM-1, WiSe 2020/2021
  • Deep Learning Methods for Inverse Problems (Sommersemester 2020)
  • Oberseminar Mathematische Parameteridentifikation (Sommersemester 2020)
  • Oberseminar Mathematische Parameteridentifikation (Wintersemester 2019/2020)
  • Oberseminar Mathematische Parameteridentifikation (Sommersemester 2019)
  • Oberseminar Mathematische Parameteridentifikation (Wintersemester 2018/2019)

 

2021

Alexander West (Bachelorarbeit Mathematik)
Parabolische partielle Differentialgleichungen auf Mannigfaltigkeiten,
Betreuer: Dr. Tobias Kluth
24.09.2021

2019

Mahir Gürsoy (Bachelorarbeit Technomathematik)
Modellunsicherheiten im Magnetic Particle Imaging – Rekonstruktion mittels Kleinste-Quadrate-Methode
Betreuer: Dr. Tobias Kluth
18.11.2019

Dennis Zvegincev (Masterarbeit Mathematik)
Joint-Motion und Bildrekonstruktion für Magnetic Particle Imaging in 2D und 3D
Betreuer: Dr. Tobias Kluth, Prof. Dr. Dr. h.c. Peter Maaß
06.03.2019

Johannes Leuschner (Masterarbeit Technomathematik)
Deep Learning in der Anwendung des Magnetic Particle Imaging
Betreuer: Dr. Tobias Kluth, Prof. Dr. Dr. h.c. Peter Maaß
11.02.2019

Hannes Albers (Masterarbeit Technomathematik)
A parameter identification problem for particle magnetization models in the context of Magnetic Particle Imaging
Betreuer: Dr. Tobias Kluth, Prof. Dr. Dr. h.c. Peter Maaß
23.01.2019

2018

Robin Görmer (Masterarbeit Mathematik)
3D Electrical Impedance Tomography
Betreuer: Dr. Tobias Kluth, Prof. Dr. Dr. h.c. Peter Maaß
30.10.2018

Judith Nickel (Bachelorarbeit Technomathematik)
Filter Functions and Approximation Errors in X-Ray Tomography
Betreuer: Prof. Dr. Dr. h.c. Peter Maaß, Dr. Tobias Kluth
21.08.2018

Nicolas Jathe (Masterarbeit Technomathematik)
Deep Neural Networks for Inverse Problems in Imaging
Betreuer: Prof. Dr. Dr. h.c. Peter Maaß, Dr. Tobias Kluth
06.08.2018

Janna Flötotto (Masterarbeit Mathematik)
Regularisierte Total-Least-Squares-Rekonstruktion beim Magnetic Particle Imaging
Betreuer: Dr. Tobias Kluth
06.04.2018

2016 - 2020 Koordinator des DFG Graduiertenkollegs π³