| 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 | 255 | 2018 |
| 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 | 53 | 2016 |
| Variational Networks: Connecting Variational Methods and Deep Learning E Kobler, T Klatzer, K Hammernik, T Pock German Conference on Pattern Recognition, 281-293, 2017 | 42 | 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 | 35 | 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 | 29 | 2017 |
| 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 | 29 | 2016 |
| 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 | 28 | 2019 |
| 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 | 24 | 2013 |
| 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 | 18 | 2015 |
| 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 | 6 | 2017 |
| Automatic Intervertebral Disc Localization and Segmentation in 3D MR Images Based on Regression Forests and Active Contours M Urschler, K Hammernik, T Ebner, D Štern International Workshop on Computational Methods and Clinical Applications …, 2015 | 6 | 2015 |
| Accelerated knee imaging using a deep learning based reconstruction F Knoll, K Hammernik, E Garwood, A Hirschmann, L Rybak, M Bruno, ... ISMRM 25th Annual Meeting, 0645, 2017 | 5 | 2017 |
| Deep Learning Methods for Parallel Magnetic Resonance Image Reconstruction F Knoll, K Hammernik, C Zhang, S Moeller, T Pock, DK Sodickson, ... arXiv preprint arXiv:1904.01112, 2019 | 4 | 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, 2018 | 3 | 2018 |
| Variational photoacoustic image reconstruction with spatially resolved projection data K Hammernik, T Pock, R Nuster Photons Plus Ultrasound: Imaging and Sensing 2017 10064, 100643I, 2017 | 3 | 2017 |
| 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 | 2 | 2018 |
| Trainable Regularization for Multi-frame Superresolution T Klatzer, D Soukup, E Kobler, K Hammernik, T Pock German Conference on Pattern Recognition, 90-100, 2017 | 2 | 2017 |
| On the influence of sampling pattern design on deep learning-based MRI reconstruction K Hammernik, F Knoll, DK Sodickson, T Pock ISMRM 25th Annual Meeting, 0644, 2017 | 2 | 2017 |
| Machine learning for image reconstruction K Hammernik, F Knoll Handbook of Medical Image Computing and Computer Assisted Intervention, 25-64, 2020 | 1 | 2020 |
| Convex framework for 2D & 3D image segmentation using shape constraints K Hammernik | 1 | 2015 |
Thomas PockProfessor of Computer Science, TU GrazVerified email at icg.tugraz.at
