Preprints
[6] 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. Preprint, arXiv:2307.10431, 2023.
[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. Preprint, arXiv:2306.01335, 2023.
[4] H. Albers, T. Kluth. Time-dependent parameter identification in a
Fokker-Planck equation based magnetization model of large ensembles of nanoparticles.
Preprint, arXiv:2307.03560, 2023.
[3] C. Brandt, T. Kluth, T. Knopp, L. Westen. Dynamic image
reconstruction with motion priors in application to 3d magnetic particle imaging. Preprint,
arXiv:2306.11625, 2023.
[2] T. Lütjen, F. Schönfeld, A. Oberacker, J. Leuschner, M. Schmidt,
A Wald, T. Kluth. Learning-based approaches for reconstructions with inexact
operators in nanoCTapplications. Preprint, arXiv:2307.10474, 2023.
[1] T. Kluth, B. Jin. Exploiting heuristic parameter choice rules for one-click image reconstruction in magnetic particle imaging. Preprint can be provided upon request, 2020.
Journal
[18] 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. In Journal of Magnetism and Magnetic Materials, 543:168534, 2022.
[17] H. Albers, T. Kluth, T. Knopp. Simulating magnetization dynamics of large ensembles of single domain nanoparticles: Numerical study of Brown/Neel dynamics and parameter identification problems in magnetic particle imaging. In Journal of Magnetism and Magnetic Materials, 541:168508, 2022.
[16] M. Möddel, F. Griese, T. Kluth, T. Knopp. Estimating the spatial orientation of immobilized magnetic nanoparticles with parallel-aligned easy axes. In Physical Review Applied , 16(4):L041003, 2021.
[15] 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. In International Journal on Magnetic Particle Imaging , 7(1):ID 2103001, 19 pages, 2021.
[14] T. Kluth, B. Jin. L1 data fitting for robust numerical reconstruction in magnetic particle imaging: quantitative evaluation on Open MPI dataset. In International Journal on Magnetic Particle Imaging , 6(2):ID 2012001, 23 pages, 2020.
[13] 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. In Inverse Problems, 36(12):124006, 2020.
[12] S. Dittmer, T. Kluth, P. Maass, D. Otero
Baguer. Regularization by architecture: A deep prior
approach for inverse problems. In Journal of Mathematical
Imaging and Vision, 62(3):456-470, 2020.
[11] T. Kluth, P. Szwargulski, T. Knopp.
Towards accurate modeling of the multidimensional magnetic
particle imaging physics. In New Journal of Physics,
21(10):103032, 2019.
[10] T. Kluth, B. Jin. Enhanced
reconstruction in magnetic particle imaging by whitening and
randomized SVD approximation. In Physics in Medicine &
Biology, 64(12):125026, 2019.
[9] J. Clemens, T. Kluth, T. Reineking.
β-SLAM: Simultaneous Localization and Grid Mapping With Beta
Distributions. Information Fusion, 52:62-75, 2019
[8] T. Kluth, B. Jin, G. Li. On the Degree
of Ill-Posedness of Multi-Dimensional Magnetic Particle
Imaging. In Inverse Problems, 34(9): 095006, 2018
[7] T. Kluth. Mathematical models for
magnetic particle imaging. In Inverse Problems, 34(8):
083001, 2018
[6] T. Kluth, P. Maass. Model uncertainty in
magnetic particle imaging: Nonlinear problem formulation and
model-based sparse reconstruction. In International
Journal on Magnetic Particle Imaging, Article ID
1707004, 3(2), 10 pages, 2017.
[5] C. Bathke, T. Kluth, C. Brandt, P.
Maass. Improved image reconstruction in magnetic
particle imaging using structural a priori information. In International
Journal on Magnetic Particle Imaging, Article ID
1703015, 3(1), 10 pages, 2017.
[4] J. Clemens, T. Reineking, T. Kluth. An
evidential approach to SLAM, path planning, and active
exploration. In International Journal of Approximate
Reasoning, 73:1-26, 2016.
[3] T. Kluth, C. Zetzsche. Numerosity as a
topological invariant. In Journal of Vision, 16(3):30,
1-39, 2016.
[2] M. Gehre, T. Kluth, C. Sebu, P.
Maass. Sparse 3D reconstructions in Electrical Impedance
Tomography using real data. In Inverse Problems in Science
and Engineering, 22(1):31-44, 2014.
[1] M. Gehre, T. Kluth, A. Lipponen, B. Jin, A.
Seppaenen, J. P. Kaipio, P. Maass. Sparsity
Reconstruction in Electrical Impedance Tomography: An
Experimental Evaluation. In Journal of Computational and
Applied Mathematics,, 236(8):2126-2136, 2012.
Conference articles (full paper, peer-reviewed)
[7] S. Dittmer, T. Kluth, D. Otero Baguer, and P. Maass. A deep prior
approach to magnetic particle imaging. In F. Deeba, P. Johnson, T. Würfl,
and J. Ye, editors, Machine Learning for Medical Image Reconstruction , pages
113-122. Springer International Publishing, 2020.
[6] T. Reineking, T. Kluth, D.
Nakath. Adaptive information selection in images:
Efficient naive bayes nearest neighbor classification. In G.
Azzopardi and N. Petkov, editors, Computer Analysis of
Images and Patterns, volume 9256 of Lecture Notes in
Computer Science, pages 350-361. Springer International
Publishing, 2015.
[5] T. Kluth, D. Nakath, T. Reineking, C. Zetzsche,
K. Schill. Affordance-based object recognition using
interactions obtained from a utility maximization principle.
In Computer Vision-ECCV 2014 Workshops, volume 8926 of
Lecture Notes in Computer Science, pages 406-412.
Springer International Publishing, 2014.
[4] T. Kluth, C. Zetzsche. Spatial numerosity:
A computational model based on a topological invariant. In C.
Freksa, B. Nebel, M. Hegarty, and T. Barkowsky, editors, Spatial
Cognition IX, volume 8684 of Lecture Notes in
Computer Science, pages 237-252. Springer International
Publishing, 2014.
[3] D. Nakath, T. Kluth, T. Reineking, C. Zetzsche,
K. Schill. Active sensorimotor object recognition in
three-dimensional space. In C. Freksa, B. Nebel, M. Hegarty,
and T. Barkowsky, editors, Spatial Cognition IX,
volume 8684 of Lecture Notes in Computer Science,
pages 312-324. Springer International Publishing, 2014.
[2] C. Zetzsche, K. Gadzicki, T.
Kluth. Statistical Invariants of Spatial Form: From Local
AND to Numerosity. In Proceedings of the Second
Interdisciplinary Workshop The Shape of Things, pp.
163-172, 2013.
[1] S. Eberhardt, T. Kluth, C. Zetzsche, K.
Schill. From Pattern Recognition to Place Detection. In Proceedings
of the International Workshop on Place-related Knowledge
Acquisition Research (P-KAR), pp. 39-44, 2012.
Conference proceedings (short contributions, peer-reviewed)
[12] H. Albers, F. Thieben, M. Boberg, K. Scheffler, T. Knopp, T. Kluth. Model-based calibration and image reconstruction with immobilized
nanoparticles. International Journal on Magnetic Particle Imaging, 9(1 Suppl. 1):ID
2303002, 5 pages, 2023.
[11] T. Knopp, H. Albers, M. Grosser, M. Möddel, T. Kluth. Exploiting the Fourier neural operator for faster magnetization model evaluations based on the Fokker-Planck equation. International Journal on Magnetic Particle Imaging, 9(1 Suppl.
1):ID 2303003, 4 pages, 2023.
[10] M. Möddel, A. Schlömerkemper, T. Kluth. Limitations of current MPI
models in the context of fluid dynamics. International Journal on Magnetic Particle
Imaging, 9(1 Suppl. 1):ID 2303078, 4 pages, 2023.
[9] H. Albers, T. Kluth. Immobilized nanoparticles with uniaxial anisotropy in
multi-dimensional lissajous-type excitation: An equilibrium model approach. International
Journal on Magnetic Particle Imaging, 8(1 Suppl. 1):ID 2203048, 4 pages, 2022.
[8] M. Nitzsche, H. Albers, T. Kluth, B. Hahn. Compensating
model imperfections during image reconstruction via RESESOP. International Journal on
Magnetic Particle Imaging, 8(1 Suppl. 1):ID 2203062, 4 pages, 2022.
[7] H. Albers, T. Kluth, T.
Knopp. MNPDynamics: A computational
toolbox for simulating magnetic moment behavior of ensembles of nanoparticles. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009020, 3 pages,
2020.
[6] T. Kluth, P. Szwargulski, T.
Knopp. Towards accurate modeling of the
multidimensional MPI physics. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009004, 3 pages,
2020.
[5] M. Möddel, F. Griese, T. Kluth, T.
Knopp. Estimating orientation
using multi-contrast MPI. International Journal on Magnetic Particle Imaging, 6(2 Suppl. 1):ID 2009023, 3 pages,
2020.
[4] T. Kluth, B. Hahn, C.
Brandt. Spatio-temporal concentration reconstruction
using motion priors in magnetic particle imaging. In T. Knopp
and T. M. Buzug, editors, 9th International Workshop on
Magnetic Particle Imaging 2019, pages 23-24. Infinite
Science Publishing, 2019.
[3] T. Kluth, B. Jin. Exploiting
ill-posedness in magnetic particle imaging - system matrix
approximation via randomized SVD. In T. Knopp and T. M. Buzug,
editors, 8th International Workshop on Magnetic Particle
Imaging 2018, pages 127-128. Infinite Science
Publishing, 2018.
[2] 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. In T. Knopp and T. M. Buzug, editors, 8th
International Workshop on Magnetic Particle Imaging 2018,
pages 53-54. Infinite Science Publishing, 2018.
[1] C. Bathke, T. Kluth, P. Maass. MPI
reconstruction using structural prior information and
sparsity. In T. Knopp and T. M. Buzug, editors, 8th
International Workshop on Magnetic Particle Imaging 2018,
pages 129-130. Infinite Science Publishing, 2018.
Misc
[7] T. Kluth, H. Albers. Simulation of non-linear magnetization effects and
parameter identification problems in magnetic particle imaging. Oberwolfach Report No. 39/2020, 2020.
[6] T. Kluth. Guest editorial: Recent developments on system function/matrix
representation, hybrid simulation techniques, and magnetic actuation. In International Journal on Magnetic
Particle Imaging, 6(1):ID 2010001, 3 pages, 2020.
[5] T. Kluth and P. Maass. Model uncertainty
in magnetic particle iamging: Motivating nonlinear problems by
model-based sparse reconstruction. In T. Knopp and T. M.
Buzug, editors, 7th International Workshop on Magnetic
Particle Imaging 2017, page 83. Infinite Science
Publishing, 2017.
[4] C. Bathke, T. Kluth, C. Brandt, P.
Maass. Improved image reconstruction in magnetic
particle imaging using structural a priori information. In T.
Knopp and T. M. Buzug, editors, 7th International Workshop
on Magnetic Particle Imaging 2017, page 85. Infinite
Science Publishing, 2017.
[3] T. Kluth, C. Zetzsche. Visual
numerosity: A computational model based on a topological
invariant. In Perception ECVP, volume 43, page 163,
2014.
[2] T. Kluth, D. Nakath, T. Reineking, C.
Zetzsche, K. Schill. Sensorimotor integration using an
information gain strategy in application to object recognition
tasks. In Perception ECVP, volume 42, page 223, 2013.
[1] S. Eberhardt, T. Kluth, M. Fahle, C.
Zetzsche. The role of nonlinearities in hierarchical
feed-forward models for pattern recognition. In Perception
ECVP, volume 41, page 241, 2012.