| 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 | 1756 | 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 | 232 | 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 | 148 | 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 | 116 | 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 | 47* | 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 | 46 | 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 | 44 | 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 | 29 | 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 | 15 | 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 | 14 | 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 | 11 | 2023 |
| 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 | 11 | 2019 |
| 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 | 10 | 2021 |
| 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 | 10 | 2018 |
| 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 | 9 | 2022 |
| Computed tomography reconstruction using generative energy-based priors M Zach, E Kobler, T Pock arXiv preprint arXiv:2203.12658, 2022 | 9 | 2022 |
| Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study A Rastogi, G Brugnara, M Foltyn-Dumitru, MA Mahmutoglu, CJ Preetha, ... The Lancet Oncology 25 (3), 400-410, 2024 | 8 | 2024 |
| 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 | 8 | 2023 |
| 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 | 8 | 2019 |
| 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 | 8 | 2019 |
Thomas PockProfessor of Computer Science, TU GrazBestätigte E-Mail-Adresse bei icg.tugraz.at
