| Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function B Ruijsink, E Puyol-Antón, I Oksuz, M Sinclair, W Bai, JA Schnabel, ... Cardiovascular Imaging 13 (3), 684-695, 2020 | 185 | 2020 |
| Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning I Oksuz, B Ruijsink, E Puyol-Antón, JR Clough, G Cruz, A Bustin, C Prieto, ... Medical image analysis 55, 136-147, 2019 | 120 | 2019 |
| Fairness in cardiac MR image analysis: an investigation of bias due to data imbalance in deep learning based segmentation E Puyol-Antón, B Ruijsink, SK Piechnik, S Neubauer, SE Petersen, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021 | 100 | 2021 |
| Free breathing whole-heart 3D CINE MRI with self-gated Cartesian trajectory M Usman, B Ruijsink, MS Nazir, G Cruz, C Prieto Magnetic resonance imaging 38, 129-137, 2017 | 85 | 2017 |
| Global and local interpretability for cardiac MRI classification JR Clough, I Oksuz, E Puyol-Antón, B Ruijsink, AP King, JA Schnabel International Conference on Medical Image Computing and Computer-Assisted …, 2019 | 83 | 2019 |
| Deep learning-based detection and correction of cardiac MR motion artefacts during reconstruction for high-quality segmentation I Oksuz, JR Clough, B Ruijsink, EP Anton, A Bustin, G Cruz, C Prieto, ... IEEE Transactions on Medical Imaging 39 (12), 4001-4010, 2020 | 78 | 2020 |
| Fairness in cardiac magnetic resonance imaging: assessing sex and racial bias in deep learning-based segmentation E Puyol-Antón, B Ruijsink, J Mariscal Harana, SK Piechnik, S Neubauer, ... Frontiers in cardiovascular medicine 9, 859310, 2022 | 69 | 2022 |
| A framework for combining a motion atlas with non-motion information to learn clinically useful biomarkers: application to cardiac resynchronisation therapy response prediction D Peressutti, M Sinclair, W Bai, T Jackson, J Ruijsink, D Nordsletten, ... Medical image analysis 35, 669-684, 2017 | 52 | 2017 |
| Estimation of cardiovascular relative pressure using virtual work-energy D Marlevi, B Ruijsink, M Balmus, D Dillon-Murphy, D Fovargue, ... Scientific reports 9 (1), 1375, 2019 | 49 | 2019 |
| Medical image computing and computer assisted intervention–MICCAI 2021 E Puyol-Antón, B Ruijsink, SK Piechnik, S Neubauer, SE Petersen, ... Springer International Publishing, Cham, 2021 | 42 | 2021 |
| Automated quantification of myocardial tissue characteristics from native T1 mapping using neural networks with uncertainty-based quality-control E Puyol-Antón, B Ruijsink, CF Baumgartner, PG Masci, M Sinclair, ... Journal of Cardiovascular Magnetic Resonance 22 (1), 60, 2020 | 41 | 2020 |
| Interpretable deep models for cardiac resynchronisation therapy response prediction E Puyol-Antón, C Chen, JR Clough, B Ruijsink, BS Sidhu, J Gould, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 41 | 2020 |
| Detection and correction of cardiac MRI motion artefacts during reconstruction from k-space I Oksuz, J Clough, B Ruijsink, E Puyol-Antón, A Bustin, G Cruz, C Prieto, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2019: 22nd …, 2019 | 41 | 2019 |
| Regional multi-view learning for cardiac motion analysis: Application to identification of dilated cardiomyopathy patients E Puyol-Antón, B Ruijsink, B Gerber, MS Amzulescu, H Langet, ... IEEE Transactions on Biomedical Engineering 66 (4), 956-966, 2018 | 39 | 2018 |
| Fully automated myocardial strain estimation from cine MRI using convolutional neural networks E Puyol-Antón, B Ruijsink, W Bai, H Langet, M De Craene, JA Schnabel, ... 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018 …, 2018 | 37 | 2018 |
| Prevalence and disease expression of pathogenic and likely pathogenic variants associated with inherited cardiomyopathies in the general population M Bourfiss, M Van Vugt, AI Alasiri, B Ruijsink, J Van Setten, AF Schmidt, ... Circulation: Genomic and Precision Medicine 15 (6), e003704, 2022 | 34 | 2022 |
| Deep learning using K-space based data augmentation for automated cardiac MR motion artefact detection I Oksuz, B Ruijsink, E Puyol-Antón, A Bustin, G Cruz, C Prieto, D Rueckert, ... Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st …, 2018 | 34 | 2018 |
| Improved passive catheter tracking with positive contrast for CMR-guided cardiac catheterization using partial saturation (pSAT) MN Velasco Forte, K Pushparajah, T Schaeffter, I Valverde Perez, ... Journal of Cardiovascular Magnetic Resonance 19, 1-9, 2017 | 32 | 2017 |
| Pressure–volume loop-derived cardiac indices during dobutamine stress: a step towards understanding limitations in cardiac output in children with hypoplastic left heart syndrome J Wong, K Pushparajah, A de Vecchi, B Ruijsink, GF Greil, T Hussain, ... International journal of cardiology 230, 439-446, 2017 | 32 | 2017 |
| Modeling left atrial flow, energy, blood heating distribution in response to catheter ablation therapy D Dillon-Murphy, D Marlevi, B Ruijsink, A Qureshi, H Chubb, E Kerfoot, ... Frontiers in Physiology 9, 1757, 2018 | 30 | 2018 |
