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
512 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
76 2019 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
71 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
65 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
51 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
48 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
34 2016 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
29 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
23 2015 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
20 2020 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
16 2019 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
12 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
8 2015 Inverse GANs for accelerated MRI reconstruction D Narnhofer, K Hammernik, F Knoll, T Pock
Wavelets and Sparsity XVIII 11138, 111381A, 2019
6 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
6 2018 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
6 2018 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
6 2017 Machine learning for image reconstruction K Hammernik, F Knoll
Handbook of Medical Image Computing and Computer Assisted Intervention, 25-64, 2020
5 2020 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
5 2017 K Hammernik, J Schlemper, C Qin, J Duan, RM Summers, D Rueckert
arXiv preprint arXiv:1912.09278, 2019
3 2019 
Thomas PockProfessor of Computer Science, TU GrazOverená e-mailová adresa na: icg.tugraz.at
