| Computationally guided personalized targeted ablation of persistent atrial fibrillation PM Boyle, T Zghaib, S Zahid, RL Ali, D Deng, WH Franceschi, JB Hakim, ... Nature biomedical engineering 3 (11), 870-879, 2019 | 262 | 2019 |
| Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia A Prakosa, HJ Arevalo, D Deng, PM Boyle, PP Nikolov, H Ashikaga, ... Nature biomedical engineering 2 (10), 732-740, 2018 | 256 | 2018 |
| Benchmarking framework for myocardial tracking and deformation algorithms: An open access database C Tobon-Gomez, M De Craene, K Mcleod, L Tautz, W Shi, A Hennemuth, ... Medical image analysis 17 (6), 632-648, 2013 | 199 | 2013 |
| Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter S Zahid, KN Whyte, EL Schwarz, RC Blake III, PM Boyle, J Chrispin, ... Heart rhythm 13 (8), 1687-1698, 2016 | 99 | 2016 |
| 3D strain assessment in ultrasound (straus): A synthetic comparison of five tracking methodologies M De Craene, S Marchesseau, B Heyde, H Gao, M Alessandrini, ... IEEE transactions on medical imaging 32 (9), 1632-1646, 2013 | 82 | 2013 |
| Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI D Deng, H Arevalo, F Pashakhanloo, A Prakosa, H Ashikaga, E McVeigh, ... Frontiers in physiology 6, 282, 2015 | 63 | 2015 |
| Generation of synthetic but visually realistic time series of cardiac images combining a biophysical model and clinical images A Prakosa, M Sermesant, H Delingette, S Marchesseau, E Saloux, ... IEEE transactions on medical imaging 32 (1), 99-109, 2012 | 60 | 2012 |
| A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction D Deng, HJ Arevalo, A Prakosa, DJ Callans, NA Trayanova EP Europace 18 (suppl_4), iv60-iv66, 2016 | 59 | 2016 |
| Methodology for image-based reconstruction of ventricular geometry for patient-specific modeling of cardiac electrophysiology A Prakosa, P Malamas, S Zhang, F Pashakhanloo, H Arevalo, DA Herzka, ... Progress in biophysics and molecular biology 115 (2-3), 226-234, 2014 | 58 | 2014 |
| Lack of regional association between atrial late gadolinium enhancement on cardiac magnetic resonance and atrial fibrillation rotors J Chrispin, EG Ipek, S Zahid, A Prakosa, M Habibi, D Spragg, JE Marine, ... Heart rhythm 13 (3), 654-660, 2016 | 57 | 2016 |
| Image‐based reconstruction of three‐dimensional myocardial infarct geometry for patient‐specific modeling of cardiac electrophysiology E Ukwatta, H Arevalo, M Rajchl, J White, F Pashakhanloo, A Prakosa, ... Medical physics 42 (8), 4579-4590, 2015 | 54 | 2015 |
| Predicting risk of sudden cardiac death in patients with cardiac sarcoidosis using multimodality imaging and personalized heart modeling in a multivariable classifier JK Shade, A Prakosa, DM Popescu, R Yu, DR Okada, J Chrispin, ... Science Advances 7 (31), eabi8020, 2021 | 52 | 2021 |
| Ventricular arrhythmia risk prediction in repaired tetralogy of Fallot using personalized computational cardiac models JK Shade, MJ Cartoski, P Nikolov, A Prakosa, A Doshi, E Binka, L Olivieri, ... Heart Rhythm 17 (3), 408-414, 2020 | 47 | 2020 |
| Electromechanical modeling of human ventricles with ischemic cardiomyopathy: numerical simulations in sinus rhythm and under arrhythmia M Salvador, M Fedele, PC Africa, E Sung, A Prakosa, J Chrispin, ... Computers in Biology and Medicine 136, 104674, 2021 | 46 | 2021 |
| Substrate spatial complexity analysis for the prediction of ventricular arrhythmias in patients with ischemic cardiomyopathy DR Okada, J Miller, J Chrispin, A Prakosa, N Trayanova, S Jones, ... Circulation: Arrhythmia and Electrophysiology 13 (4), e007975, 2020 | 46 | 2020 |
| An incompressible log-domain demons algorithm for tracking heart tissue K McLeod, A Prakosa, T Mansi, M Sermesant, X Pennec International Workshop on Statistical Atlases and Computational Models of …, 2011 | 46 | 2011 |
| The fibrotic substrate in persistent atrial fibrillation patients: comparison between predictions from computational modeling and measurements from focal impulse and rotor mapping PM Boyle, JB Hakim, S Zahid, WH Franceschi, MJ Murphy, A Prakosa, ... Frontiers in Physiology 9, 1151, 2018 | 43 | 2018 |
| Personalized digital-heart technology for ventricular tachycardia ablation targeting in hearts with infiltrating adiposity E Sung, A Prakosa, KN Aronis, S Zhou, SL Zimmerman, H Tandri, ... Circulation: Arrhythmia and Electrophysiology 13 (12), e008912, 2020 | 41 | 2020 |
| Fat infiltration in the infarcted heart as a paradigm for ventricular arrhythmias E Sung, A Prakosa, S Zhou, RD Berger, J Chrispin, S Nazarian, ... Nature cardiovascular research 1 (10), 933-945, 2022 | 40 | 2022 |
| Sensitivity of ablation targets prediction to electrophysiological parameter variability in image-based computational models of ventricular tachycardia in post-infarction patients D Deng, A Prakosa, J Shade, P Nikolov, NA Trayanova Frontiers in Physiology 10, 628, 2019 | 40 | 2019 |
Maxime SermesantInria, Université Côte d'Azur - IHU Liryc - 3IA Côte d'AzurEmail verificata su inria.fr