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Dr. Tobias Kluth

Bild Tobias Kluth

Dr. Tobias Kluth

Research associate

Inverse Probleme und Magnetic Particle Imaging

Bibliothekstraße 5
28359 Bremen

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

Research areas

  • Inverse Problems
  • Parameter identification
  • Mathematical modelling and simluation
  • Deep Learning
  • Scientific computing

Projects

  • DELETO - Machine learning in correlative MR and high-throughput NanoCT
  • D-MPI - Dynamic Inverse Problems in Magnetic Particle Imaging
  • Research Training Group π³ - Parameter Identification – Analysis, Algorithms, Applications
  • MPI² - Model-based parameter identification in Magnetic Particle Imaging

 

Journal articles

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, T. Knopp.
A simulation framework for particle magnetization dynamics of large ensembles of single domain particles: Numerical treatment of Brown/Néel dynamics and parameter identification problems in magnetic particle imaging.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2010.07772

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.
Zur Veröffentlichung eingereicht.
online unter: https://arxiv.org/abs/2007.01593

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

 

Conference proceedings

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 Industrial Mathematics, University of Bremen, "3D Electrical Impedance Tomography with Sparsity Constraints".
2011 - 2015PhD Student, Cognitive Informatics, University of Bremen
2015Promotion zum Dr.-Ing., Universität Bremen, "Intrinsic dimensionality in vision: Nichtlinearer Filterentwurf und Anwendungen."
Betreuer: Dr. Christoph Zetzsche.
since 2016Postdoc at the Center for Industrial Mathematics, University of Bremen
  • 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)

 

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 Coordinator of the Research Training Group π³