| 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 | 1918 | 2018 |
| Deep-Learning Methods for Parallel Magnetic Resonance Imaging Reconstruction: A Survey of the Current Approaches, Trends, and Issues F Knoll, K Hammernik, C Zhang, S Moeller, T Pock, DK Sodickson, ... IEEE Signal Processing Magazine 37 (1), 128-140, 2020 | 366 | 2020 |
| 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 | 258 | 2019 |
| CINENet: deep learning-based 3D cardiac CINE MRI reconstruction with multi-coil complex-valued 4D spatio-temporal convolutions T Küstner, N Fuin, K Hammernik, A Bustin, H Qi, R Hajhosseiny, PG Masci, ... Scientific reports 10 (1), 13710, 2020 | 219 | 2020 |
| 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 | 153 | 2017 |
| A multi-center milestone study of clinical vertebral CT segmentation J Yao, JE Burns, D Forsberg, A Seitel, A Rasoulian, P Abolmaesumi, ... Computerized Medical Imaging and Graphics 49, 16-28, 2016 | 148 | 2016 |
| A deep learning architecture for limited-angle computed tomography reconstruction K Hammernik, T Würfl, T Pock, A Maier Bildverarbeitung für die Medizin 2017: Algorithmen-Systeme-Anwendungen …, 2017 | 105 | 2017 |
| Learning Joint Demosaicing and Denoising Based on Sequential Energy Minimization T Klatzer, K Hammernik, P Knobelreiter, T Pock Computational Photography (ICCP), 2016 IEEE International Conference on, 1-11, 2016 | 101 | 2016 |
| Systematic evaluation of iterative deep neural networks for fast parallel MRI reconstruction with sensitivity‐weighted coil combination K Hammernik, J Schlemper, C Qin, J Duan, RM Summers, D Rueckert Magnetic Resonance in Medicine 86 (4), 1859-1872, 2021 | 96 | 2021 |
| Physics-Driven Deep Learning for Computational Magnetic Resonance Imaging: Combining physics and machine learning for improved medical imaging K Hammernik, T Küstner, B Yaman, Z Huang, D Rueckert, F Knoll, ... IEEE Signal Processing Magazine 40 (1), 98-114, 2023 | 78* | 2023 |
| Learning Diffeomorphic and Modality-invariant Registration using B-splines H Qiu, C Qin, A Schuh, K Hammernik, D Rueckert Medical Imaging with Deep Learning, 2021 | 78 | 2021 |
| Inverse GANs for accelerated MRI reconstruction D Narnhofer, K Hammernik, F Knoll, T Pock Wavelets and Sparsity XVIII 11138, 381-392, 2019 | 59 | 2019 |
| Cardiac MR: from theory to practice TF Ismail, W Strugnell, C Coletti, M Božić-Iven, S Weingaertner, ... Frontiers in cardiovascular medicine 9, 826283, 2022 | 51 | 2022 |
| Vertebrae Segmentation in 3D CT Images Based on a Variational Framework K Hammernik, T Ebner, D Stern, M Urschler, T Pock Recent Advances in Computational Methods and Clinical Applications for Spine …, 2015 | 51 | 2015 |
| Spray Drying of Aqueous Salbutamol Sulfate Solutions Using the Nano Spray Dryer B-90—The Impact of Process Parameters on Particle Size EM Littringer, S Zellnitz, K Hammernik, V Adamer, H Friedl, NA Urbanetz Drying Technology 31 (12), 1346-1353, 2013 | 51 | 2013 |
| 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 | 50 | 2021 |
| Complementary time‐frequency domain networks for dynamic parallel MR image reconstruction C Qin, J Duan, K Hammernik, J Schlemper, T Küstner, R Botnar, C Prieto, ... Magnetic Resonance in Medicine 86 (6), 3274-3291, 2021 | 45 | 2021 |
| Deep learning for retrospective motion correction in MRI: a comprehensive review V Spieker, H Eichhorn, K Hammernik, D Rueckert, C Preibisch, ... IEEE Transactions on Medical Imaging, 2023 | 42 | 2023 |
| Cooperative training and latent space data augmentation for robust medical image segmentation C Chen, K Hammernik, C Ouyang, C Qin, W Bai, D Rueckert Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 41 | 2021 |
| Machine learning for image reconstruction K Hammernik, F Knoll Handbook of Medical Image Computing and Computer Assisted Intervention, 25-64, 2020 | 40 | 2020 |
Daniel RueckertTechnical University of Munich and Imperial College LondonΗ διεύθυνση ηλεκτρονικού ταχυδρομείου έχει επαληθευτεί στον τομέα tum.de