Erich Kobler
Erich Kobler
Co-Groupleader at Department of Neuroradiology, University Hospital Bonn
Verified email at
Cited by
Cited by
Learning a variational network for reconstruction of accelerated MRI data
K Hammernik, T Klatzer, E Kobler, MP Recht, DK Sodickson, T Pock, ...
Magnetic resonance in medicine 79 (6), 3055-3071, 2018
Assessment of the generalization of learned image reconstruction and the potential for transfer learning
F Knoll, K Hammernik, E Kobler, T Pock, MP Recht, DK Sodickson
Magnetic resonance in medicine 81 (1), 116-128, 2019
Variational networks: connecting variational methods and deep learning
E Kobler, T Klatzer, K Hammernik, T Pock
Pattern Recognition: 39th German Conference, GCPR 2017, Basel, Switzerland …, 2017
Total deep variation for linear inverse problems
E Kobler, A Effland, K Kunisch, T Pock
Proceedings of the IEEE/CVF Conference on computer vision and pattern …, 2020
Total deep variation: A stable regularization method for inverse problems
E Kobler, A Effland, K Kunisch, T Pock
IEEE transactions on pattern analysis and machine intelligence 44 (12), 9163 …, 2021
Variational networks: An optimal control approach to early stopping variational methods for image restoration
A Effland, E Kobler, K Kunisch, T Pock
Journal of mathematical imaging and vision 62, 396-416, 2020
Bayesian uncertainty estimation of learned variational MRI reconstruction
D Narnhofer, A Effland, E Kobler, K Hammernik, F Knoll, T Pock
IEEE transactions on medical imaging 41 (2), 279-291, 2021
Accelerating prostate diffusion-weighted MRI using a guided denoising convolutional neural network: retrospective feasibility study
EA Kaye, EA Aherne, C Duzgol, I Häggström, E Kobler, Y Mazaheri, ...
Radiology: Artificial Intelligence 2 (5), e200007, 2020
Reduction of gadolinium-based contrast agents in MRI using convolutional neural networks and different input protocols: limited interchangeability of synthesized sequences with …
R Haase, T Pinetz, Z Bendella, E Kobler, D Paech, W Block, A Effland, ...
Investigative radiology 58 (6), 420-430, 2023
Variational deep learning for low-dose computed tomography
E Kobler, M Muckley, B Chen, F Knoll, K Hammernik, T Pock, D Sodickson, ...
2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018
SparseCT: System concept and design of multislit collimators
B Chen, E Kobler, MJ Muckley, AD Sodickson, T O'Donnell, T Flohr, ...
Medical physics 46 (6), 2589-2599, 2019
Variational adversarial networks for accelerated MR image reconstruction
K Hammernik, E Kobler, T Pock, MP Recht, DK Sodickson, F Knoll
Joint Annual Meeting ISMRM-ESMRMB, 2018
Constrained and unconstrained deep image prior optimization models with automatic regularization
P Cascarano, G Franchini, E Kobler, F Porta, A Sebastiani
Computational Optimization and Applications 84 (1), 125-149, 2023
Image morphing in deep feature spaces: Theory and applications
A Effland, E Kobler, T Pock, M Rajković, M Rumpf
Journal of mathematical imaging and vision 63, 309-327, 2021
Investigation of implicit constitutive relations in which both the stress and strain appear linearly, adjacent to non-penetrating cracks
H Itou, VA Kovtunenko, KR Rajagopal
Mathematical Models and Methods in Applied Sciences 32 (07), 1475-1492, 2022
Joint reconstruction and classification of tumor cells and cell interactions in melanoma tissue sections with synthesized training data
A Effland, E Kobler, A Brandenburg, T Klatzer, L Neuhäuser, M Hölzel, ...
International journal of computer assisted radiology and surgery 14, 587-599, 2019
Explicit diffusion of Gaussian mixture model based image priors
M Zach, T Pock, E Kobler, A Chambolle
International Conference on Scale Space and Variational Methods in Computer …, 2023
Computed tomography reconstruction using generative energy-based priors
M Zach, E Kobler, T Pock
arXiv preprint arXiv:2203.12658, 2022
Shared prior learning of energy-based models for image reconstruction
T Pinetz, E Kobler, T Pock, A Effland
SIAM Journal on Imaging Sciences 14 (4), 1706-1748, 2021
Joint multi-anatomy training of a variational network for reconstruction of accelerated magnetic resonance image acquisitions
PM Johnson, MJ Muckley, M Bruno, E Kobler, K Hammernik, T Pock, ...
Machine Learning for Medical Image Reconstruction: Second International …, 2019
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