Publikationen von Dr. Tobias Kluth

Buchkapitel (1)

1. P. Maaß, S. Dittmer, T. Kluth, J. Leuschner, M. Schmidt.
   Mathematische Architekturen für Neuronale Netze.
   Erfolgsformeln – Anwendungen der Mathematik, M. Ehrhardt, M. Günther, W. Schilders (Hrsg.), Mathematische Semesterberichte, S. 190-195, Springer Verlag, 2022.

   DOI: 10.1007/s00591-022-00325-y

Zeitschriftenartikel (21)

1. 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 unter: https://iopscience.iop.org/article/10.1088/1361-6420/ad0660

2. 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

3. 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

4. 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

5. 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.
6. 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.
   Inverse Problems, 36(12), 2020.

   DOI: https://doi.org/10.1088/1361-6420/abc2fe

7. T. Kluth, B. Jin.
   L1 data fitting for robust reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset.
   International Journal on Magnetic Particle Imaging, , 2020.

   DOI: 10.18416/IJMPI.2020.2012001
   online unter: https://journal.iwmpi.org/index.php/iwmpi/article/view/146

8. 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

9. 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, 62(3):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

10. T. Kluth, H. Albers.
    Simulation of non-linear magnetization effects and parameter identification problems in magnetic particle imaging.
    Erscheint in Oberwolfach Reports
11. 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

12. 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

13. 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

14. T. Kluth.
    Mathematical models for magnetic particle imaging.
    Inverse Problems, 34(8), 2018.

    DOI: 10.1088/1361-6420/aac535

15. T. Kluth, B. Jin, G. Li.
    On the Degree of Ill-Posedness of Multi-Dimensional Magnetic Particle Imaging.
    Inverse Problems, 34(9), 2018.

    DOI: 10.1088/1361-6420/aad015

16. 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

17. 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

18. 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.
19. T. Kluth, C. Zetzsche.
    Numerosity as a topological invariant.
    Journal of Vision, 16(3):1-39, 2016.
20. 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

21. 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 (5)

1. 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

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

4. 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

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

Qualifikationsarbeiten (1)

1. T. Kluth.
   Intrinsic dimensionality in vision: Nonlinear filter design and applications.
   Dissertationsschrift, Universität Bremen, 2015.

   online unter: http://nbn-resolving.de/urn:nbn:de:gbv:46-00104628-17

Tagungsbeiträge (18)

1. 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

2. 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

3. 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.

   DOI: 10.1007/978-3-030-61598-7_11

4. 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.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009023

5. 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.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):3 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009020

6. 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.
   International Journal on Magnetic Particle Imaging, T. Knopp, T. M. Buzug (Hrsg.), 6(2):2 pages, Infinite Science Publishing, 2020.

   DOI: 10.18416/IJMPI.2020.2009004

7. 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.
8. 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.
9. 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.
10. 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.
11. 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.
12. 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.
13. 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, 2016, London, Großbritannien.
    Lecture Notes in Computer Science, Proceedings CAIP, 9256:350-361, 2015.

    DOI: 10.1007/978-3-319-23192-1_29

14. 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.
15. 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.
16. 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.
17. 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.
18. 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.