Publications

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Publication about openCARP

Plank, G., Loewe A., Neic. A et al. (2021). The openCARP simulation environment for cardiac electrophysiology. Computer Methods and Programs in Biomedicine 2021;208:106223. doi:10.1016/j.cmpb.2021.106223

The list below is autogenerated from the works citing this work. Not all of them necessarily used openCARP directly.

Publications using openCARP

1. Berg, L. A., Oliveira, R. S., Camps, J., Lima, L. M. R. de, Oliveira Campos, J. de, Wang, Z. J., Doste, R., Bueno-Orovio, A., Santos, R. W. dos, and Rodriguez, B., Toward cardiac electrophysiology digital twins with an efficient open source scalable solver on GPU clusters. Scientific Reports 2026;16: doi:10.1038/s41598-025-33709-w.
2. Brown, A. L., Liu, J., Ennis, D. B., and Marsden, A. L., Cardiac mechanics modeling: Recent developments and current challenges. Journal of Elasticity 2026;158: doi:10.1007/s10659-026-10204-5.
3. Denham, N., Massé, S., Abderrahman, Y., Asta, J., Lai, P., Rodriguez, H., Tsoref, L., Nemaire, M., Anderson, R., Vigmond, E., and Nanthakumar, K., Principal component–referenced multipolar mapping to localize an arrhythmic source from various depths of the myocardium. JACC: Clinical Electrophysiology 2026;12:785–798. doi:10.1016/j.jacep.2025.10.016.
4. Gibbs, C. E., and Boyle, P. M., Accelerated intrinsic beating rate in heterogeneously coupled human pluripotent stem cell-derived cardiomyocytes can underlie focal ventricular tachycardia in regenerative therapy. The Journal of Precision Medicine: Health and Disease 2026;6:100035. doi:10.1016/j.premed.2026.100035.
5. Mamajiwala, M., Corrado, C., Lanyon, C. W., Niederer, S. A., Wilkinson, R. D., and Clayton, R. H., Rapid calibration of atrial electrophysiology models using gaussian process emulators in the ensemble kalman filter. Scientific Reports 2026;16: doi:10.1038/s41598-026-39948-9.
6. Obada, G., Ogbomo-Harmitt, S., Deprez, M., and Aslanidi, O., “Machine learning highlights left atrial fibrotic heterogeneity as a key predictor of atrial fibrillation inducibility,” 2026 in Statistical Atlases and Computational Models of the Heart. Regular and CMRxRecon Challenge Papers (Springer Nature Switzerland), 249–260. doi:10.1007/978-3-032-17734-6_24.
7. Orós-Rodrigo, S., Fu, J., Greiner, J., Madl, J., Linder, M., Zgierski-Johnston, C., Loewe, A., Kohl, P., and Rog-Zielinska, E., Cardiomyocyte mechanical activity counteracts intraluminal calcium depletion in the transverse-axial tubular system during fast electrical stimulation. 2026; doi:10.64898/2026.01.09.698373.
8. Roney, C., Plank, G., Honarbakhsh, S., Vidal Horrach, C., Pezzuto, S., and Vigmond, E., Recovering intrinsic conduction velocity and action potential duration from electroanatomic mapping data using curvature. Medical Image Analysis 2026;107:103809. doi:10.1016/j.media.2025.103809.
9. Sakata, K., Prakosa, A., Yamamoto, C. A. P., Ali, S. Y., Mohsen, Y., Loeffler, S., Kholmovski, E. G., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Relationships between three-dimensional fibrosis distribution, atrial adiposity, and voltage abnormalities associated with persistent atrial fibrillation. Europace 2026a;28: doi:10.1093/europace/euag060.
10. Sakata, K., Yamamoto, C. A. P., Ali, S. Y., Loeffler, S., Prakosa, A., Tice, B. M., Kholmovski, E. G., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Assessment of persistent atrial fibrillation arrhythmogenesis in the right atrium using digital twins. Heart Rhythm 2026b;23:e123–e132. doi:10.1016/j.hrthm.2025.10.052.
11. Walton, R. D., Renard, E., Haïssaguerre, M., Benoist, D., Chaigne, S., Charron, S., Cheniti, G., Constantin, M., Denis, A., Douard, M., Dubes, V., Duchateau, J., Guicheney, P., Guillot, B., et al., Endocardial i overexpression and fibrotic remodeling underlying pause-dependent early repolarization in humans. JACC: Clinical Electrophysiology 2026; doi:10.1016/j.jacep.2026.01.022.
12. Xin, L., Haiying, L., Yanhong, C., Shiqi, Y., Linsheng, H., and Jian, W., Triggers and maintenance of idiopathic atrial fibrillation: A multiscale computational simulation study. Computer Methods and Programs in Biomedicine 2026;274:109173. doi:10.1016/j.cmpb.2025.109173.
13. Zappon, E., Azzolin, L., Gsell, M. A. F., Thaler, F., Prassl, A. J., Arnold, R., Gillette, K., Kariman, M., Manninger, M., Scherr, D., Neic, A., Urschler, M., Augustin, C. M., Vigmond, E. J., et al., An efficient end-to-end computational framework for the generation of ECG calibrated volumetric models of human atrial electrophysiology. Medical Image Analysis 2026;107:103822. doi:10.1016/j.media.2025.103822.
14. Zhang, K., Magtibay, K., Trayanova, N., and Vigmond, E., A model of β‐adrenergic stimulation in human ventricular cells for tissue‐scale simulations of sympathetically modulated tachycardias. The Journal of Physiology 2026;604:1897–1914. doi:10.1113/jp289340.
15. Zhou, B., Balmus, M., Corrado, C., Cicci, L., Qian, S., and Niederer, S. A., TorchCor: High-performance cardiac electrophysiology simulations with the finite element method on GPUs. SoftwareX 2026;33:102521. doi:10.1016/j.softx.2026.102521.
16. Loewe, A., Hunter, P. J., and Kohl, P., Computational modelling of biological systems now and then: Revisiting tools and visions from the beginning of the century. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 2025;383: doi:10.1098/rsta.2023.0384.
17. Sakata, K., Yamamoto, C. A. P., Prakosa, A., Tice, B. M., Ali, S. Y., Loeffler, S., Kholmovski, E. G., Sinha, S. K., Marine, J. E., Calkins, H., Spragg, D. D., and Trayanova, N. A., Digital twins enable stratification of persistent atrial fibrillation patients for ablation diminishing unnecessary heart damage. npj Digital Medicine 2025;8: doi:10.1038/s41746-025-01625-y.
18. Sánchez, J., Llorente-Lipe, I., Espinosa, C. B., Loewe, A., Hernández-Romero, I., Vicente-Puig, J., Ros, S., Atienza, F., Carta-Bergaz, A., Climent, A. M., and Guillem, M. S., Enhancing premature ventricular contraction localization through electrocardiographic imaging and cardiac digital twins. Computers in Biology and Medicine 2025;190:109994. doi:10.1016/j.compbiomed.2025.109994.
19. Sobota, V., Stoks, J., Patel, K. H. K., Shetty, R., Ni, H., Grandi, E., Ng, F. S., Volders, P. G. A., Cluitmans, M. J. M., and Bayer, J. D., The apicobasal dispersion of ventricular repolarization in humans is associated with age and affects arrhythmia vulnerability. The Journal of Physiology 2025; doi:10.1113/jp288356.
20. Antoniou, C.-K., Karampinos, K., Tsiachris, D., Kordalis, A., Arsenos, P., Doundoulakis, I., Dilaveris, P., Milaras, N., Sideris, S., Kariki, O., Kasiakogias, A., Vlachopoulos, C., Toutouzas, K., Tsioufis, K., et al., Current trends in virtual electrophysiology use for risk stratification and treatment of ventricular arrhythmias. Frontiers in Cardiovascular Medicine 2025;12: doi:10.3389/fcvm.2025.1709175.
21. Banduc, T., Azzolin, L., Manninger, M., Scherr, D., Plank, G., Pezzuto, S., and Sahli Costabal, F., Simulation-free prediction of atrial fibrillation inducibility with the fibrotic kernel signature. Medical Image Analysis 2025;99:103375. doi:10.1016/j.media.2024.103375.
22. Barrios Espinosa, C., Sánchez, J., Appel, S., Becker, S., Krauß, J., Martínez Díaz, P., Unger, L., Houillon, M., and Loewe, A., A cyclical fast iterative method for simulating reentries in cardiac electrophysiology using an eikonal-based model. Engineering with Computers 2025;41:2335–2358. doi:10.1007/s00366-024-02094-9.
23. Bergquist, J., A. Orkild, B., Kwan, E., Gillette, K., Yazaki, K., Plank, G., Elhabian, S., MacLeod, R., and Ranjan, R., Comparison of LGE MRI scar identification methods for atrial computational modeling. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.166.
24. Bezerra, A. S., Hendrickx, S., Van den Abeele, R., Wülfers, E. M., Verstraeten, B., Lootens, S., Okenov, A., Nezlobinsky, T., Knecht, S., Duytschaever, M., Segers, V. F. M., and Vandersickel, N., Cross-correlation as an alternative for local activation times for the analysis of reentries in directed graph mapping. Biomedical Signal Processing and Control 2025;106:107716. doi:10.1016/j.bspc.2025.107716.
25. Biasi, N., Seghetti, P., Parollo, M., Zucchelli, G., and Tognetti, A., A matlab toolbox for cardiac electrophysiology simulations on patient-specific geometries. Computers in Biology and Medicine 2025a;185:109529. doi:10.1016/j.compbiomed.2024.109529.
26. Biasi, N., Vultaggio, D. M., Seghetti, P., Laurino, M., and Tognetti, A., A matlab platform for pacemaker algorithm assessment based on anatomically detailed closed-loop computer modeling. Engineering with Computers 2025b;41:4511–4527. doi:10.1007/s00366-025-02214-z.
27. Bifulco, S. F., Magoon, M. J., Chahine, Y., Kim, I., Macheret, F., Akoum, N., and Boyle, P. M., Predicting arrhythmia recurrence post-ablation in atrial fibrillation using explainable machine learning. Communications Medicine 2025;5: doi:10.1038/s43856-025-01058-4.
28. Bishop, M. J., and Plank, G., Stochastic virtual heart model predictions. Nature Cardiovascular Research 2025;4:539–542. doi:10.1038/s44161-025-00641-1.
29. Campos, F. O., Wijesuriya, N., Elliott, M. K., Vere, F. de, Howell, S., Strocchi, M., Monaci, S., Whitaker, J., Plank, G., Rinaldi, C. A., and Bishop, M. J., In silico pace mapping identifies pacing sites more accurately than inverse body surface potential mapping. Heart Rhythm 2025;22:1790–1799. doi:10.1016/j.hrthm.2024.12.036.
30. Centofanti, E., Huynh, N. M. M., Pavarino, L. F., and Scacchi, S., Parallel algebraic multigrid solvers for composite discontinuous galerkin discretization of the cardiac EMI model in heterogeneous media. Computer Methods in Applied Mechanics and Engineering 2025;442:118001. doi:10.1016/j.cma.2025.118001.
31. Corrado, C., Roney, C. H., Narayan, S. M., Giles, W. R., and Niederer, S. A., The effect of clinically relevant changes in extracellular electrolyte concentrations on human atrial arrhythmias. Communications Medicine 2025;6: doi:10.1038/s43856-025-01260-4.
32. Costa de Almeida, R., Almonfrey, D., Squara, F., and Zarzoso, V., Generation of realistic synthetic electrograms for atrial fibrillation analysis. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.436.
33. Denham, N. C., Suszko, A. M., Nemaire, M., Bhaskaran, A., Massé, S., Nanthakumar, K., Haïssaguerre, M., Downar, E., Vigmond, E., and Chauhan, V. S., Microvolt activation alternans identifies conduction velocity heterogeneity and the entrance site of ischemic ventricular tachycardia. JACC: Clinical Electrophysiology 2025;11:1114–1128. doi:10.1016/j.jacep.2025.02.014.
34. Dong, R., Fu, Z., Zhang, C., Liu, Y., Wang, Y., Zhang, N., Wang, Z., Hou, J., Xia, L., Wu, Y., Zhou, S., and Deng, D., Comparative analysis of the ten tusscher and tomek human ventricular cell models at cellular, tissue, and organ levels: Implications for post‐infarct ventricular tachycardia simulation. Physiological Reports 2025;13: doi:10.14814/phy2.70435.
35. Finsberg, H., Fenicsx-beat - an open source simulation framework for cardiac electrophysiology. Journal of Open Source Software 2025;10:8416. doi:10.21105/joss.08416.
36. Gibbs, C. E., and Boyle, P. M., Population-based computational simulations elucidate mechanisms of focal arrhythmia following stem cell injection. Journal of Molecular and Cellular Cardiology 2025;204:5–16. doi:10.1016/j.yjmcc.2025.04.010.
37. Houillon, M., Klar, J., Boutanios, Z., Stary, T., Cojean, T., Anzt, H., and Loewe, A., FACILE-RS: Archiving and long-term preservation of research software repositories made easy. Journal of Open Source Software 2025;10:7330. doi:10.21105/joss.07330.
38. Huethorst, E., Bishop, M. J., Burton, F. L., Denning, C., Gadegaard, N., Myles, R. C., and Smith, G. L., Evidence for intermittent coupling of intramyocardial small, engineered heart tissues acutely implanted into rabbit myocardium. Cardiovascular Research 2025;121:1697–1711. doi:10.1093/cvr/cvaf034.
39. Jones, C. E., and Oomen, P. J. A., Synergistic biophysics and machine learning modeling to rapidly predict cardiac growth probability. Computers in Biology and Medicine 2025;184:109323. doi:10.1016/j.compbiomed.2024.109323.
40. Jung, A., Augustin, C. M., Voglhuber-Höller, J., Kiessling, M., Ljubojevic-Holzer, S., Mirams, G. R., Niederer, S. A., and Plank, G., Computational modelling for improved translation of cardiac inotropic and lusitropic drug effects from rats to humans. Journal of Pharmacological and Toxicological Methods 2025;134:107747. doi:10.1016/j.vascn.2025.107747.
41. Kariman, M., Gillette, K., Gsell, M. A. F., Prassl, A. J., Plank, G., and Augustin, C. M., Computational modelling of the impact of anatomical changes on ECGs in left ventricular hypertrophy. The Journal of Physiology 2025;603:5387–5413. doi:10.1113/jp287954.
42. Kröner, J., Maffezzoli, F., Sassi, R., and W Rivolta, M., Spatial variability of catheter positions affects omnipolar mapping in 2D atrial sheet simulations. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.381.
43. Kruthoff, C., Sánchez, J., Krauß, J., Gerach, T., Barrios Espinosa, C., and Loewe, A., Evaluating the effects of left bundle branch block in an electromechanical heart model. Current Directions in Biomedical Engineering 2025;11:389–392. doi:10.1515/cdbme-2025-0199.
44. Langen, J. S., Boyle, P. M., Malan, D., and Sasse, P., Optogenetic quantification of cardiac excitability and electrical coupling in intact hearts to explain cardiac arrhythmia initiation. Science Advances 2025;11: doi:10.1126/sciadv.adt4103.
45. Lee, J. D., Nguyen, A., Gibbs, C. E., Jin, Z. R., Wang, Y., Moghadasi, A., Wait, S. J., Choi, H., Evitts, K. M., Asencio, A., Bremner, S. B., Zuniga, S., Chavan, V., Pranoto, I. K. A., et al., Monitoring in real time and far-red imaging of H2O2 dynamics with subcellular resolution. Nature Chemical Biology 2025;22:556–567. doi:10.1038/s41589-025-01891-7.
46. Linder, M., Stary, T., Bitay, G., Nagy, N., and Loewe, A., Sympathetic stimulation can compensate for hypocalcaemia‐induced bradycardia in human and rabbit sinoatrial node cells. The Journal of Physiology 2025; doi:10.1113/jp287557.
47. Lootens, S., Van den Abeele, R., Kappadan, V., Handa, B., Duytschaever, M., Knecht, S., Luik, A., Haas, A., Wülfers, E. M., Bezerra, A. S., Verstraeten, B., Hendrickx, S., Okenov, A., Nezlobinsky, T., et al., Detection of regular rotational activity during cardiac arrhythmia using the helmholtz decomposition for directed graphs. Journal of Molecular and Cellular Cardiology 2025;204:40–54. doi:10.1016/j.yjmcc.2025.05.002.
48. Lydon, H., Kazemi, M., Bishop, M., and Paoletti, N., SimICD: A closed-loop simulation framework for ICD therapy. 2025 in 2025 47th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE), 1–7. doi:10.1109/embc58623.2025.11254859.
49. Marín, J., Baptiste, T. M. G., Rodero, C., Williams, S. E., Niederer, S. A., and García-Fernández, I., SciBlend: Advanced data visualization workflows within blender. Computers & Graphics 2025;130:104264. doi:10.1016/j.cag.2025.104264.
50. Marins de Lima, L., Sachetto Oliveira, R., Campos, F., Arantes Berg, L., Oliveira Campos, J. de, and Weber dos Santos, R., Benchmarking open cardiac electrophysiology simulators: MonoAlg3D and OpenCARP. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.376.
51. Obada, G., Ogbomo-Harmitt, S., Deprez, M., Van Den Abeelen, R., Vandersickel, N., and Aslanidi, O., “Rotor core projection ablation (RCPA): Novel computational approach to catheter ablation therapy for atrial fibrillation,” 2025 in Statistical Atlases and Computational Models of the Heart. Workshop, CMRxRecon and MBAS Challenge Papers. (Springer Nature Switzerland), 313–322. doi:10.1007/978-3-031-87756-8_31.
52. Reimer, J., Sundnes, J., and Spiteri, R. J., “The effects of domain size and electrode placement on electrical excitability in the bidomain model,” 2025 in Scientific Computing and Software (Springer Nature Switzerland), 1–15. doi:10.1007/978-3-031-85288-6_1.
53. Stein, J., Greene, D., Fenton, F., and Shiferaw, Y., Mechanism of arrhythmogenesis driven by early after depolarizations in cardiac tissue. PLOS Computational Biology 2025;21:e1012635. doi:10.1371/journal.pcbi.1012635.
54. Tanner, L. C. R., Busatto, A., Bergquist, J. A., Good, W. W., Zenger, B., Plank, G., Narayan, A., Gillette, K., and MacLeod, R. S., Uncertainty quantification via polynomial chaos expansion of myocardial fibre orientation and cardiac activation patterns. The Journal of Physiology 2025; doi:10.1113/jp287746.
55. Thangamani, A., Loechner, V., and Genaud, S., “Extending polygeist to generate OpenMP SIMD and GPU MLIR code,” 2025 in Euro-Par 2024: Parallel Processing Workshops (Springer Nature Switzerland), 323–328. doi:10.1007/978-3-031-90203-1_36.
56. Van Den Abeele, R., Lootens, S., Verstraeten, B., Bezerra, A. S., Okenov, A., Nezlobinskii, T., Van Nieuwenhuize, V., Hendrickx, S., and Vandersickel, N., Paired reentries maintain ventricular tachycardia: A topological analysis of arrhythmic mechanisms using the index theorem. Frontiers in Network Physiology 2025;5: doi:10.3389/fnetp.2025.1638085.
57. Vidal Horrach, C., Coveney, S., Ahmed Jaffery, O., Ehnesh, M., Niederer, S., Honarbakhsh, S., Narayan, S., and H. Roney, C., Automated workflow to integrate electroanatomic maps into patient-specific bi-atrial models for personalized AF treatment. 2025 in 2025 Computing in Cardiology Conference (CinC) CinC2025. (Computing in Cardiology). doi:10.22489/cinc.2025.385.
58. Waight, M. C., Prakosa, A., Li, A. C., Bunce, N., Marciniak, A., Trayanova, N. A., and Saba, M. M., Personalized heart digital twins detect substrate abnormalities in scar-dependent ventricular tachycardia. Circulation 2025a;151:521–533. doi:10.1161/circulationaha.124.070526.
59. Waight, M. C., Prakosa, A., Li, A. C., Truong, A., Bunce, N., Marciniak, A., Trayanova, N. A., and Saba, M. M., Heart digital twins predict features of invasive reentrant circuits and ablation lesions in scar-dependent ventricular tachycardia. Circulation: Arrhythmia and Electrophysiology 2025b;18: doi:10.1161/circep.124.013660.
60. Yang, H., Cheng, Y., Zhao, P., Cai, J., Yin, Z., Chen, S., Guo, G., Zhu, C., Liu, K., and Zu, L., Uncover hidden physical information of soft matter by observing large deformation. Advanced Science 2025;12: doi:10.1002/advs.202414526.
61. Zelieska, L., Ondrusova, B., Tysler, M., and Sánchez, J., In silico investigation of the impact of the ventricular filling heterogeneity on the electrical field propagation using a patient-specific model. 2025 in 2025 15th International Conference on Measurement (IEEE), 283–286. doi:10.23919/measurement66999.2025.11078685.
62. Zenger, B., Smith, T. W., Hicks, S., Ng, S., Pavek, T., Knutson, N., Samson, P. P., Zheng, J., Berberet, C., Ibrahim, E.-S. H., Jani, V., Tabor, J., Wilson, L. D., Jordan, S. D., et al., STAR locally prolongs effective refractory period and increases ventricular tachycardia cycle length without short-term scar formation or functional decline: Insights from a translational porcine model study. Circulation: Arrhythmia and Electrophysiology 2025;18: doi:10.1161/circep.124.013684.
63. Zhang, Y., Prakosa, A., Zhang, K., Carrick, R., Chrispin, J., Zimmerman, S., Aronis, K., Kholmovski, E., Tichnell, C., Murray, B., James, C., Calkins, H., and Trayanova, N., Genotype-specific digital twins for arrhythmia ablation targeting in arrhythmogenic right ventricular cardiomyopathy. 2025; doi:10.21203/rs.3.rs-8006874/v1.
64. Zhu, E., and Yang, S., Towards human digital twin: Reviewing human modelling and simulation. Journal of Industrial Information Integration 2025;48:100975. doi:10.1016/j.jii.2025.100975.
65. Zolotarev, A. M., Johnson, K., Mohammad, Y., Alwazzan, O., Slabaugh, G., and Roney, C. H., Synthetic fibrosis distributions for data augmentation in predicting atrial fibrillation ablation outcomes: An in silico study. Frontiers in Cardiovascular Medicine 2025;12: doi:10.3389/fcvm.2025.1512356.
66. Dake, P. G., Mukherjee, J., Sahu, K. C., and Pandit, A. B., Computational fluid dynamics in cardiovascular engineering: A comprehensive review. Transactions of the Indian National Academy of Engineering 2024;9:335–362. doi:10.1007/s41403-024-00478-3.
67. Abrasheva, V. O., Kovalenko, S. G., Slotvitsky, M., Romanova, S. А., Aitova, A. A., Frolova, S., Tsvelaya, V., and Syunyaev, R. A., Human sodium current voltage‐dependence at physiological temperature measured by coupling a patch‐clamp experiment to a mathematical model. The Journal of Physiology 2024;602:633–661. doi:10.1113/jp285162.
68. Arnold, R., Prassl, A. J., Neic, A., Thaler, F., Augustin, C. M., Gsell, M. A. F., Gillette, K., Manninger, M., Scherr, D., and Plank, G., pyCEPS: A cross-platform electroanatomic mapping data to computational model conversion platform for the calibration of digital twin models of cardiac electrophysiology. Computer Methods and Programs in Biomedicine 2024;254:108299. doi:10.1016/j.cmpb.2024.108299.
69. Barnafi, N. A., Huynh, N. M. M., Pavarino, L. F., and Scacchi, S., Robust parallel nonlinear solvers for implicit time discretizations of the bidomain equations with staggered ionic models. Computers & Mathematics with Applications 2024;167:134–149. doi:10.1016/j.camwa.2024.04.014.
70. Becker, S., Alberto Barrios Espinosa, C., Anna Unger, L., and Loewe, A., Influence of conduction velocity restitution steepness on atrial fibrillation vulnerability and maintenance. 2024 in 2024 Computing in Cardiology Conference (CinC) CinC2024. (Computing in Cardiology). doi:10.22489/cinc.2024.105.
71. Bergquist, J., A. Orkild, B., N. Paccione, E., Kwan, E., Zenger, B., Hunt, B., Yazaki, K., MacLeod, R., Narayan, A., and Ranjan, R., Uncertainty quantification of fibrotic conductivity effects on computational model-derived ablation of atypical left atrial flutter. 2024 in 2024 Computing in Cardiology Conference (CinC) CinC2024. (Computing in Cardiology). doi:10.22489/cinc.2024.021.
72. Berndt, A., Lee, J., Nguyen, A., Jin, Z., Moghadasi, A., Gibbs, C., Wait, S., Evitts, K., Asencio, A., Bremner, S., Zuniga, S., Chavan, V., Williams, A., Smith, A., et al., Far-red and sensitive sensor for monitoring real time H2O2 dynamics with subcellular resolution and in multi-parametric imaging applications. 2024; doi:10.21203/rs.3.rs-3974015/v1.
73. Bertrand, A., Yamamoto, C., Monopoli, G., Schrotter, T., Myklebust, L., Uv, J. J., Arevalo, H. J., and Maleckar, M. M., “Augmentation of cardiac ischemic geometry for improving machine learning performance in arrhythmic risk stratification,” 2024 in Computational Physiology (Springer Nature Switzerland), 39–53. doi:10.1007/978-3-031-53145-3_3.
74. Brown, A. L., Sexton, Z. A., Hu, Z., Yang, W., and Marsden, A. L., “Computational approaches for mechanobiology in cardiovascular development and diseases,” 2024 in Heart Development and Disease (Elsevier), 19–50. doi:10.1016/bs.ctdb.2024.01.006.
75. Cluitmans, M. J. M., Plank, G., and Heijman, J., Digital twins for cardiac electrophysiology: State of the art and future challenges. Herzschrittmachertherapie + Elektrophysiologie 2024;35:118–123. doi:10.1007/s00399-024-01014-0.
76. Dharmaprani, D., Tiver, K., Salari Shahrbabaki, S., Jenkins, E. V., Chapman, D., Strong, C., Quah, J. X., Tonchev, I., O’Loughlin, L., Mitchell, L., Tung, M., Ahmad, W., Stoyanov, N., Aguilar, M., et al., Observable atrial and ventricular fibrillation episode durations are conformant with a power law based on system size and spatial synchronization. Circulation: Arrhythmia and Electrophysiology 2024;17: doi:10.1161/circep.123.012684.
77. Fehrentz, T., Amin, E., Görldt, N., Strasdeit, T., Moussavi‐Torshizi, S., Leippe, P., Trauner, D., Meyer, C., Frey, N., Sasse, P., and Klöcker, N., Optical control of cardiac electrophysiology by the photochromic ligand azobupivacaine 2. British Journal of Pharmacology 2024;182:1125–1142. doi:10.1111/bph.17394.
78. Finsberg, H., and Hake, J., Gotranx: General ODE translator. Journal of Open Source Software 2024;9:7063. doi:10.21105/joss.07063.
79. Göbel, F., Cojean, T., and Anzt, H., “BDDC preconditioning on GPUs for cardiac simulations,” 2024 in Euro-Par 2023: Parallel Processing Workshops (Springer Nature Switzerland), 265–268. doi:10.1007/978-3-031-48803-0_30.
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166. Sira Romitti, M., “Giada and Romero de Antonio, Pau and Liberos Mascarell, Alejandro and Serra Almor, Dolors and García Fernandez, Ignacio and Lozano Ibañez, Miguel and Sebastian Aguilar, Rafael and Rodrigo Bort”, Effects of long- and short-term memory on action potential duration for atrial cellular automata. 2022 in 2022 Computing in Cardiology Conference (CinC) CinC2022. (Computing in Cardiology). doi:10.22489/cinc.2022.258.
167. Skupien, N., Barrios Espinosa, C., Dössel, O., and Loewe, A., Refining the eikonal model to reproduce the influence of atrial tissue geometry on conduction velocity. Current Directions in Biomedical Engineering 2022;8:133–136. doi:10.1515/cdbme-2022-1035.
168. Sutanto, H., and Heijman, J., Integrative computational modeling of cardiomyocyte calcium handling and cardiac arrhythmias: Current status and future challenges. Cells 2022;11:1090. doi:10.3390/cells11071090.
169. Zhu, C., Vedula, V., Parker, D., Wilson, N., Shadden, S., and Marsden, A., svFSI: A multiphysics package for integrated cardiac modeling. Journal of Open Source Software 2022;7:4118. doi:10.21105/joss.04118.
170. Bifulco, S. F., Scott, G. D., Sarairah, S., Birjandian, Z., Roney, C. H., Niederer, S. A., Mahnkopf, C., Kuhnlein, P., Mitlacher, M., Tirschwell, D., Longstreth, W., Akoum, N., and Boyle, P. M., Computational modeling identifies embolic stroke of undetermined source patients with potential arrhythmic substrate. eLife 2021;10: doi:10.7554/elife.64213.
171. Azzolin, L., Schuler, S., Dössel, O., and Loewe, A., A reproducible protocol to assess arrhythmia vulnerability in silico: Pacing at the end of the effective refractory period. Frontiers in Physiology 2021;12: doi:10.3389/fphys.2021.656411.
172. Beach, M., Sim, I., Mehta, A., Kotadia, I., O’Hare, D., Whitaker, J., Solis-Lemus, J. A., Razeghi, O., Chiribiri, A., O’Neill, M., Williams, S., Niederer, S. A., and Roney, C. H., Using the universal atrial coordinate system for MRI and electroanatomic data registration in patient-specific left atrial model construction and simulation. 2021 in Functional Imaging and Modeling of the Heart, eds. D. B. Ennis, L. E. Perotti, and V. Y. Wang (Cham: Springer International Publishing), 629–638.
173. Coveney, S., Corrado, C., Oakley, J. E., Wilkinson, R. D., Niederer, S. A., and Clayton, R. H., Bayesian calibration of electrophysiology models using restitution curve emulators. Frontiers in Physiology 2021;12: doi:10.3389/fphys.2021.693015.
174. Dıaz, P. M., Azzolin, L., Arciniégas, J. P. S., Nagel, C., Dössel, O., and Loewe, A., Influence of the right atrium for arrhythmia vulnerability: Geometry inference using a statistical shape model. 2021; doi:10.5445/IR/1000138456.
175. Dössel, O., Luongo, G., Nagel, C., and Loewe, A., Computer modeling of the heart for ECG interpretation—a review. Hearts 2021;2:350–368. doi:10.3390/hearts2030028.
176. Espinosa, C. B., Skupien, N., Kachel, G., Dössel, O., and Loewe, A., Influence of wave-front and atrial tissue properties on eikonal model simulations. 2021; doi:10.5445/IR/1000138447.
177. Luongo, G., Azzolin, L., Schuler, S., Rivolta, M. W., Almeida, T. P., Martínez, J. P., Soriano, D. C., Luik, A., Müller-Edenborn, B., Jadidi, A., Dössel, O., Sassi, R., Laguna, P., and Loewe, A., Machine learning enables noninvasive prediction of atrial fibrillation driver location and acute pulmonary vein ablation success using the 12-lead ECG. Cardiovascular Digital Health Journal 2021;2:126–136. doi:10.1016/j.cvdhj.2021.03.002.
178. Monbaliu, R., A meandering spiral due to early afterdepolarizations as possible mechanism of torsade de pointes. 2021;
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180. Ochs, A. R., Karathanos, T. V., Trayanova, N. A., and Boyle, P. M., Optogenetic stimulation using anion channelrhodopsin (GtACR1) facilitates termination of reentrant arrhythmias with low light energy requirements: A computational study. Frontiers in Physiology 2021;12: doi:10.3389/fphys.2021.718622.
181. Sánchez, J., Luongo, G., Nothstein, M., Unger, L. A., Saiz, J., Trenor, B., Luik, A., Dössel, O., and Loewe, A., Using machine learning to characterize atrial fibrotic substrate from intracardiac signals with a hybrid in silico and in vivo dataset. Frontiers in Physiology 2021a;12: doi:10.3389/fphys.2021.699291.
182. Sánchez, J., Trenor, B., Saiz, J., Dössel, O., and Loewe, A., Fibrotic remodeling during persistent atrial fibrillation: In silico investigation of the role of calcium for human atrial myofibroblast electrophysiology. Cells 2021b;10:2852. doi:10.3390/cells10112852.
183. Schicketanz, L., Unger, L. A., Sánchez, J., Dössel, O., and Loewe, A., Separating atrial near fields and atrial far fields in simulated intra-atrial electrograms. Current Directions in Biomedical Engineering 2021;7:175–178. doi:10.1515/cdbme-2021-2045.
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Publications using CARP/CARPentry

For a list of publications using CARP/CARPentry, the predecessor of openCARP, see the website of the Computational Cardiology Lab (Medical University of Graz, Austria).

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