### Preprints

[2] T. Kluth, H. Albers. Simulation of non-linear magnetization effects and
parameter identification problems in magnetic particle imaging. To appear in Oberwolfach Report No. 39/2020, 2020.

[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)

[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

[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.