| Results of the 2020 fastMRI challenge for machine learning MR image reconstruction MJ Muckley, B Riemenschneider, A Radmanesh, S Kim, G Jeong, J Ko, ... IEEE transactions on medical imaging 40 (9), 2306-2317, 2021 | 233 | 2021 |
| State-of-the-art machine learning MRI reconstruction in 2020: Results of the second fastMRI challenge MJ Muckley, B Riemenschneider, A Radmanesh, S Kim, G Jeong, J Ko, ... arXiv preprint arXiv:2012.06318 2 (6), 7, 2020 | 41 | 2020 |
| Multi-coil mri reconstruction challenge—assessing brain mri reconstruction models and their generalizability to varying coil configurations Y Beauferris, J Teuwen, D Karkalousos, N Moriakov, M Caan, G Yiasemis, ... Frontiers in neuroscience 16, 919186, 2022 | 28 | 2022 |
| i-RIM applied to the fastMRI challenge P Putzky, D Karkalousos, J Teuwen, N Miriakov, B Bakker, M Caan, ... arXiv preprint arXiv:1910.08952, 2019 | 27 | 2019 |
| Evaluation of the robustness of learned MR image reconstruction to systematic deviations between training and test data for the models from the fastMRI challenge PM Johnson, G Jeong, K Hammernik, J Schlemper, C Qin, J Duan, ... Machine Learning for Medical Image Reconstruction: 4th International …, 2021 | 16 | 2021 |
| Direct: Deep image reconstruction toolkit G Yiasemis, N Moriakov, D Karkalousos, M Caan, J Teuwen Journal of Open Source Software 7 (73), 4278, 2022 | 13 | 2022 |
| Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction D Karkalousos, S Noteboom, HE Hulst, FM Vos, MWA Caan Physics in Medicine & Biology 67 (12), 124001, 2022 | 11 | 2022 |
| A unified model for reconstruction and R2* mapping of accelerated 7T data using the quantitative recurrent inference machine C Zhang, D Karkalousos, PL Bazin, BF Coolen, H Vrenken, JJ Sonke, ... NeuroImage 264, 119680, 2022 | 9 | 2022 |
| Multi-channel MR reconstruction (MC-MRRec) challenge—Comparing accelerated MR reconstruction models and assessing their genereralizability to datasets collected with different … Y Beauferris, J Teuwen, D Karkalousos, N Moriakov, M Caan, ... arXiv preprint arXiv:2011.07952, 2020 | 7 | 2020 |
| MultiTask Learning for accelerated-MRI Reconstruction and Segmentation of Brain Lesions in Multiple Sclerosis D Karkalousos, I Isgum, H Marquering, MWA Caan Medical Imaging with Deep Learning, 991-1005, 2024 | 1 | 2024 |
| Reconstructing unseen modalities and pathology with an efficient Recurrent Inference Machine D Karkalousos, K Lønning, HE Hulst, SO Dumoulin, JJ Sonke, FM Vos, ... arXiv preprint arXiv:2012.07819, 2020 | 1 | 2020 |
| ATOMMIC: An Advanced Toolbox for Multitask Medical Imaging Consistency to facilitate Artificial Intelligence applications from acquisition to analysis in Magnetic Resonance Imaging D Karkalousos, I Išgum, HA Marquering, MWA Caan arXiv preprint arXiv:2404.19665, 2024 | | 2024 |
| The Advanced Toolbox for Multitask Medical Imaging Consistency (ATOMMIC): A framework to facilitate Deep Learning in Magnetic Resonance Imaging D Karkalousos, I Isgum, H Marquering, MWA Caan Medical Imaging with Deep Learning, 2024 | | 2024 |
| Data Consistency for Magnetic Resonance Imaging D Karkalousos, M Caan | | 2021 |
| Recurrent Variational Inference for fast and robust reconstruction of accelerated FLAIR MRI in Multiple Sclerosis D Karkalousos, LC Liebrand, S Noteboom, HE Hulst, FM Vos, MWA Caan | | |
| Results of the 2020 fastMRI Brain Reconstruction Challenge B Riemenschneider, M Muckley, A Radmanesh, S Kim, G Jeong, J Ko, ... | | |
| A Deep Learning Accelerated MRI Reconstruction Model's Dependence on Training Data Distribution D Karkalousos, K Lønning, S Dumoulin, JJ Sonke, MWA Caan | | |
