| MedGAN: Medical image translation using GANs K Armanious, C Jiang, M Fischer, T Küstner, T Hepp, K Nikolaou, ... Computerized medical imaging and graphics 79, 101684, 2020 | 631 | 2020 |
| 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, 13710, 2020 | 199 | 2020 |
| A whole-body FDG-PET/CT Dataset with manually annotated Tumor Lesions S Gatidis, T Hepp, M Früh, C La Fougère, K Nikolaou, C Pfannenberg, ... Scientific Data 9 (1), 1-7, 2022 | 156 | 2022 |
| Retrospective correction of motion‐affected MR images using deep learning frameworks T Küstner, K Armanious, J Yang, B Yang, F Schick, S Gatidis Magnetic resonance in medicine 82 (4), 1527-1540, 2019 | 142 | 2019 |
| Unsupervised Medical Image Translation Using Cycle-MedGAN K Armanious, C Jiang, S Abdulatif, T Küstner, S Gatidis, B Yang European Association for Signal Processing (EUSIPCO), 2019 | 128 | 2019 |
| Automated reference-free detection of motion artifacts in magnetic resonance images T Küstner, A Liebgott, L Mauch, P Martirosian, F Bamberg, K Nikolaou, ... Magnetic Resonance Materials in Physics, Biology and Medicine 31, 243-256, 2018 | 108 | 2018 |
| MR-based respiratory and cardiac motion correction for PET imaging T Küstner, M Schwartz, P Martirosian, S Gatidis, F Seith, C Gilliam, T Blu, ... Medical Image Analysis, 2017 | 84 | 2017 |
| A machine-learning framework for automatic reference-free quality assessment in MRI T Küstner, S Gatidis, A Liebgott, M Schwartz, L Mauch, P Martirosian, ... Magnetic resonance imaging 53, 134-147, 2018 | 70 | 2018 |
| Deep learning applications in magnetic resonance imaging: has the future become present? S Gassenmaier, T Küstner, D Nickel, J Herrmann, R Hoffmann, ... Diagnostics 11 (12), 2181, 2021 | 66 | 2021 |
| Independent attenuation correction of whole body [18F]FDG-PET using a deep learning approach with Generative Adversarial Networks K Armanious, T Hepp, T Küstner, H Dittmann, K Nikolaou, C La Fougère, ... EJNMMI research 10, 1-9, 2020 | 66 | 2020 |
| Simultaneous multislice diffusion‐weighted MRI of the liver: Analysis of different breathing schemes in comparison to standard sequences J Taron, P Martirosian, M Erb, T Kuestner, NF Schwenzer, H Schmidt, ... Journal of Magnetic Resonance Imaging 44 (4), 865-879, 2016 | 66 | 2016 |
| Feasibility and implementation of a deep learning MR reconstruction for TSE sequences in musculoskeletal imaging J Herrmann, G Koerzdoerfer, D Nickel, M Mostapha, M Nadar, ... Diagnostics 11 (8), 1484, 2021 | 59 | 2021 |
| MR image reconstruction using a combination of compressed sensing and partial Fourier acquisition: ESPReSSo T Küstner, C Würslin, S Gatidis, P Martirosian, K Nikolaou, NF Schwenzer, ... IEEE transactions on medical imaging 35 (11), 2447-2458, 2016 | 59 | 2016 |
| Deep-learning based super-resolution for 3D isotropic coronary MR angiography in less than a minute T Küstner, CM Escobar, A Psenicny, A Bustin, N Fuin, H Qi, R Neji, ... Magnetic Resonance in Medicine, 2021 | 56 | 2021 |
| A multi-scale variational neural network for accelerating motion-compensated whole-heart 3D coronary MR angiography N Fuin, A Bustin, T Küstner, I Oksuz, J Clough, AP King, JA Schnabel, ... Magnetic resonance imaging 70, 155-167, 2020 | 51 | 2020 |
| Deep learning‐based automated abdominal organ segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies T Kart, M Fischer, T Küstner, T Hepp, F Bamberg, S Winzeck, B Glocker, ... Investigative Radiology 56 (6), 401-408, 2021 | 50 | 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 | 44 | 2023 |
| Cardiac MR: from theory to practice TF Ismail, W Strugnell, C Coletti, M Božić-Iven, S Weingaertner, ... Frontiers in cardiovascular medicine 9, 826283, 2022 | 44 | 2022 |
| Fully automated and standardized segmentation of adipose tissue compartments via deep learning in 3D whole-body MRI of epidemiologic cohort studies T Küstner, T Hepp, M Fischer, M Schwartz, A Fritsche, HU Häring, ... Radiology: Artificial Intelligence 2 (6), e200010, 2020 | 44 | 2020 |
| Acceleration of magnetic resonance cholangiopancreatography using compressed sensing at 1.5 and 3 T: a clinical feasibility study J Taron, J Weiss, M Notohamiprodjo, T Kuestner, F Bamberg, E Weiland, ... Investigative radiology 53 (11), 681-688, 2018 | 44 | 2018 |
Sergios GatidisStanford MedicineVerified email at stanford.edu
