Multi-centre, multi-vendor and multi-disease cardiac segmentation: the M&Ms challenge VM Campello, P Gkontra, C Izquierdo, C Martin-Isla, A Sojoudi, PM Full, ... IEEE Transactions on Medical Imaging 40 (12), 3543-3554, 2021 | 270 | 2021 |
Coronary microvascular dysfunction is associated with myocardial ischemia and abnormal coronary perfusion during exercise H Rahman, M Ryan, M Lumley, B Modi, H McConkey, H Ellis, C Scannell, ... Circulation 140 (22), 1805-1816, 2019 | 134 | 2019 |
High-resolution cardiac magnetic resonance imaging techniques for the identification of coronary microvascular dysfunction H Rahman, CM Scannell, OM Demir, M Ryan, H McConkey, H Ellis, ... JACC: Cardiovascular Imaging 14 (5), 978-986, 2021 | 76 | 2021 |
Deep‐learning‐based preprocessing for quantitative myocardial perfusion MRI CM Scannell, M Veta, ADM Villa, EC Sammut, J Lee, M Breeuwer, ... Journal of Magnetic Resonance Imaging 51 (6), 1689-1696, 2020 | 69 | 2020 |
Importance of operator training and rest perfusion on the diagnostic accuracy of stress perfusion cardiovascular magnetic resonance ADM Villa, L Corsinovi, I Ntalas, X Milidonis, C Scannell, G Di Giovine, ... Journal of Cardiovascular Magnetic Resonance 20 (1), 74, 2018 | 49 | 2018 |
Optimal use of vasodilators for diagnosis of microvascular angina in the cardiac catheterization laboratory H Rahman, OM Demir, M Ryan, H McConkey, C Scannell, H Ellis, A Webb, ... Circulation: Cardiovascular Interventions 13 (6), e009019, 2020 | 39 | 2020 |
Robust non-rigid motion compensation of free-breathing myocardial perfusion MRI data CM Scannell, ADM Villa, J Lee, M Breeuwer, A Chiribiri IEEE transactions on medical imaging 38 (8), 1812-1820, 2019 | 38 | 2019 |
Physics-informed neural networks for myocardial perfusion MRI quantification RLM van Herten, A Chiribiri, M Breeuwer, M Veta, CM Scannell Medical Image Analysis 78, 102399, 2022 | 34 | 2022 |
Domain-Adversarial Learning for Multi-Centre, Multi-Vendor, and Multi-Disease Cardiac MR Image Segmentation CM Scannell, A Chiribiri, M Veta International Workshop on Statistical Atlases and Computational Models of …, 2020 | 30 | 2020 |
Hierarchical Bayesian myocardial perfusion quantification CM Scannell, A Chiribiri, ADM Villa, M Breeuwer, J Lee Medical image analysis 60, 101611, 2020 | 25 | 2020 |
Optimized automated cardiac MR scar quantification with GAN‐based data augmentation DR Lustermans, S Amirrajab, M Veta, M Breeuwer, CM Scannell Computer Methods and Programs in Biomedicine 226, 107116, 2022 | 13 | 2022 |
Automated quantitative stress perfusion cardiac magnetic resonance in pediatric patients CM Scannell, H Hasaneen, G Greil, T Hussain, R Razavi, J Lee, ... Frontiers in Pediatrics 9, 2021 | 13 | 2021 |
Brief research report: quantitative analysis of potential coronary microvascular disease in suspected long-COVID syndrome P Doeblin, F Steinbeis, CM Scannell, C Goetze, S Al-Tabatabaee, J Erley, ... Frontiers in Cardiovascular Medicine 9, 877416, 2022 | 12 | 2022 |
Automatic Myocardial Disease Prediction From Delayed-Enhancement Cardiac MRI and Clinical Information A Lourenço, E Kerfoot, I Grigorescu, CM Scannell, M Varela, TM Correia International Workshop on Statistical Atlases and Computational Models of …, 2020 | 11 | 2020 |
Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging CM Scannell, T Correia, ADM Villa, T Schneider, J Lee, M Breeuwer, ... Scientific Reports 10 (1), 12684, 2020 | 9 | 2020 |
CardiSort: a convolutional neural network for cross vendor automated sorting of cardiac MR images RP Lim, S Kachel, ADM Villa, L Kearney, N Bettencourt, AA Young, ... European radiology 32 (9), 5907-5920, 2022 | 6 | 2022 |
Deep learning-based prediction of kinetic parameters from myocardial perfusion MRI CM Scannell, P van den Bosch, A Chiribiri, J Lee, M Breeuwer, M Veta Medical Imaging with Deep Learning: MIDL (Extended Abstract Track), 2019 | 6 | 2019 |
AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance CM Scannell, E Alskaf, N Sharrack, R Razavi, S Ourselin, AA Young, ... European Heart Journal-Digital Health 4 (1), 12-21, 2023 | 5 | 2023 |
Deep learning applications in myocardial perfusion imaging, a systematic review and meta-analysis E Alskaf, U Dutta, CM Scannell, A Chiribiri Informatics in Medicine Unlocked 32, 101055, 2022 | 5 | 2022 |
High-resolution free-breathing quantitative first-pass perfusion cardiac MR using dual-echo Dixon with spatio-temporal acceleration J Tourais, CM Scannell, T Schneider, E Alskaf, R Crawley, F Bosio, ... Frontiers in Cardiovascular Medicine 9, 884221, 2022 | 4 | 2022 |