| 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 | 944 | 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 | 141* | 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 | 132 | 2019 |
| Variational Networks: Connecting Variational Methods and Deep Learning E Kobler, T Klatzer, K Hammernik, T Pock German Conference on Pattern Recognition, 281-293, 2017 | 107 | 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 | 101 | 2016 |
| A Deep Learning Architecture for Limited-Angle Computed Tomography Reconstruction K Hammernik, T Würfl, T Pock, A Maier Bildverarbeitung für die Medizin 2017, 92-97, 2017 | 70 | 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 | 69 | 2016 |
| 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), 1-13, 2020 | 60 | 2020 |
| 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 | 37 | 2013 |
| Learning a Variational Model for Compressed Sensing MRI Reconstruction K Hammernik, F Knoll, D Sodickson, T Pock Proceedings of the International Society of Magnetic Resonance in Medicine …, 2016 | 33 | 2016 |
| 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 | 32 | 2015 |
| Inverse GANs for accelerated MRI reconstruction D Narnhofer, K Hammernik, F Knoll, T Pock Wavelets and Sparsity XVIII 11138, 111381A, 2019 | 25 | 2019 |
| Machine learning for image reconstruction K Hammernik, F Knoll Handbook of Medical Image Computing and Computer Assisted Intervention, 25-64, 2020 | 22 | 2020 |
| L2 or not L2: impact of loss function design for deep learning MRI reconstruction K Hammernik, F Knoll, DK Sodickson, T Pock ISMRM 25th Annual Meeting, 0687, 2017 | 22 | 2017 |
| 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 | 18 | 2021 |
| -net: Systematic Evaluation of Iterative Deep Neural Networks for Fast Parallel MR Image Reconstruction K Hammernik, J Schlemper, C Qin, J Duan, RM Summers, D Rueckert arXiv preprint arXiv:1912.09278, 2019 | 18 | 2019 |
| 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, 2021 | 12 | 2021 |
| Σ-net: ensembled iterative deep neural networks for accelerated parallel MR image reconstruction K Hammernik, J Schlemper, C Qin, J Duan, RM Summers, D Rueckert Proceedings of the ISMRM & SMRT Virtual Conference & Exhibition 2020, 0987, 2020 | 12* | 2020 |
| CG‐SENSE revisited: Results from the first ISMRM reproducibility challenge O Maier, SH Baete, A Fyrdahl, K Hammernik, S Harrevelt, L Kasper, ... Magnetic Resonance in Medicine 85 (4), 1821-1839, 2021 | 11 | 2021 |
| System, method and computer-accessible medium for learning an optimized variational network for medical image reconstruction F Knoll, K Hammernik, T Pock, DK Sodickson US Patent 10,671,939, 2020 | 10 | 2020 |
Thomas PockProfessor of Computer Science, TU GrazVerified email at icg.tugraz.at
