Wondering how to cite openCARP?

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. Azzolin, L., Eichenlaub, M., Nagel, C., Nairn, D., Sánchez, J., Unger, L., Arentz, T., Westermann, D., Dössel, O., Jadidi, A., and Loewe, A., AugmentA: Patient-specific augmented atrial model generation tool. Computerized Medical Imaging and Graphics 2023;108:102265. doi:10.1016/j.compmedimag.2023.102265.
2. Fitzen, N. A., Dı́az, P. M., Dössel, O., and Loewe, A., Impact of the right atrium on arrhythmia vulnerability. Current Directions in Biomedical Engineering 2023;9:142–145. doi:10.1515/cdbme-2023-1036.
3. Lyu, Y., Bennamoun, M., Sharif, N., Lip, G. Y. H., and Dwivedi, G., Artificial intelligence in the image-guided care of atrial fibrillation. Life 2023;13:1870. doi:10.3390/life13091870.
4. Moinuddin, A., Ali, S. Y., Goel, A., Sethi, Y., Patel, N., Kaka, N., Satapathy, P., Sah, R., Barboza, J. J., and Suhail, M. K., The age of computational cardiology and future of long-term ablation target prediction for ventricular tachycardia. Front. Cardiovasc. Med. 2023;10: doi:10.3389/fcvm.2023.1233991.
5. Bifulco, S. F., Macheret, F., Scott, G. D., Akoum, N., and Boyle, P. M., Explainable machine learning to predict anchored reentry substrate created by persistent atrial fibrillation ablation in computational models. JAHA 2023;12: doi:10.1161/jaha.123.030500.
6. Gillette, K., Gsell, M. A. F., Nagel, C., Bender, J., Winkler, B., Williams, S. E., Bär, M., Schäffter, T., Dössel, O., Plank, G., and Loewe, A., MedalCare-XL: 16,900 healthy and pathological synthetic 12 lead ECGs from electrophysiological simulations. Sci Data 2023;10: doi:10.1038/s41597-023-02416-4.
7. Grandits, T., Augustin, C. M., Haase, G., Jost, N., Mirams, G. R., Niederer, S. A., Plank, G., Varró, A., Virág, L., and Jung, A., Neural network emulation of the human ventricular cardiomyocyte action potential: A tool for more efficient computation in pharmacological studies. 2023; doi:10.1101/2023.08.16.553497.
8. Ochs, A. R., and Boyle, P. M., Optogenetic modulation of arrhythmia triggers: Proof-of-concept from computational modeling. Cel. Mol. Bioeng. 2023;16:243–259. doi:10.1007/s12195-023-00781-z.
9. Solı́s-Lemus, J. A., Baptiste, T., Barrows, R., Sillett, C., Gharaviri, A., Raffaele, G., Razeghi, O., Strocchi, M., Sim, I., Kotadia, I., Bodagh, N., O’Hare, D., O’Neill, M., Williams, S. E., et al., Evaluation of an open-source pipeline to create patient-specific left atrial models: A reproducibility study. Computers in Biology and Medicine 2023;162:107009. doi:10.1016/j.compbiomed.2023.107009.
10. Leenknegt, L., Panfilov, A. V., and Dierckx, H., Impact of electrode orientation, myocardial wall thickness, and myofiber direction on intracardiac electrograms: Numerical modeling and analytical solutions. Front. Physiol. 2023;14: doi:10.3389/fphys.2023.1213218.
11. Abrasheva, V. O., Kovalenko, S. G., Slotvitsky, M., Scherbina, S. A., Aitova, A. A., Frolova, S., Tsvelaya, V., and Syunyaev, R. A., Human sodium current voltage-dependence at physiological temperature measured by coupling patch-clamp experiment to a mathematical model. 2023; doi:10.1101/2023.06.06.543894.
12. Moussavi-Torshizi, S. E., Amin, E., and Klöcker, N., Sex-specific repolarization heterogeneity in mouse left ventricle: Optical mapping combined with mathematical modeling predict the contribution of specific ionic currents. Physiological Reports 2023;11: doi:10.14814/phy2.15670.
13. Pikunov, A. V., Syunyaev, R. A., Ali, R., Prakosa, A., Boyle, P. M., Steckmeister, V., Kutschka, I., Rytkin, E., Voigt, N., Trayanova, N., and Efimov, I. R., The role of structural<i>vs</i>cellular remodeling in arrhythmogenesis: Personalized computer models of atrial fibrillation. 2023; doi:10.1101/2023.05.13.540632.
14. Gibbs, C. E., Marchianó, S., Zhang, K., Yang, X., Murry, C. E., and Boyle, P. M., Grafthost coupling changes can lead to engraftment arrhythmia: A computational study. The Journal of Physiology 2023;601:2733–2749. doi:10.1113/jp284244.
15. Petras, A., Gsell, M. A. F., Augustin, C. M., Rodriguez-Padilla, J., Jung, A., Strocchi, M., Prinzen, F. W., Niederer, S. A., Plank, G., and Vigmond, E. J., Mechanoelectric effects in healthy cardiac function and under left bundle branch block pathology. Computers in Biology and Medicine 2023;156:106696. doi:10.1016/j.compbiomed.2023.106696.
16. Reimer, J., Domı́nguez-Rivera, S. A., Sundnes, J., and Spiteri, R. J., Physiological accuracy in simulating refractory cardiac tissue: The volume-averaged bidomain model vs. The cell-based EMI model. 2023; doi:10.1101/2023.04.10.536323.
17. Sung, E., Kyranakis, S., Daimee, U. A., Engels, M., Prakosa, A., Zhou, S., Nazarian, S., Zimmerman, S. L., Chrispin, J., and Trayanova, N. A., Evaluation of a deep learning-enabled automated computational heart modelling workflow for personalized assessment of ventricular arrhythmias. The Journal of Physiology 2023; doi:10.1113/jp284125.
18. Ryzhii, M., and Ryzhii, E., A compact multi-functional model of the rabbit atrioventricular node with dual pathways. Front. Physiol. 2023;14: doi:10.3389/fphys.2023.1126648.
19. Nagel, C., Espinosa, C. B., Gillette, K., Gsell, M. A. F., Sanchez, J., Plank, G., Dossel, O., and Loewe, A., Comparison of propagation models and forward calculation methods on cellular, tissue and organ scale atrial electrophysiology. IEEE Trans. Biomed. Eng. Transactions on Biomedical Engineering 2023;70:511–522. doi:10.1109/tbme.2022.3196144.
20. Thangamani, A., Jost, T. T., Loechner, V., Genaud, S., and Bramas, B., Lifting code generation of cardiac physiology simulation to novel compiler technology. 2023 in Proceedings of the 21st ACM/IEEE International Symposium on Code Generation and Optimization (ACM). doi:10.1145/3579990.3580008.
21. Finsberg, H. N. T., Herck, I. G. M. van, Daversin-Catty, C., Arevalo, H., and Wall, S., Simcardems: A FEniCS-based cardiac electro-mechanics solver. JOSS 2023;8:4753. doi:10.21105/joss.04753.
22. Lindner, L. P., Gerach, T., Jahnke, T., Loewe, A., Weiss, D., and Wieners, C., Efficient time splitting schemes for the monodomain equation in cardiac electrophysiology. Numer Methods Biomed Eng 2023;39: doi:10.1002/cnm.3666.
23. Ramlugun, G. S., Kulkarni, K., Pallares-Lupon, N., Boukens, B. J., Efimov, I. R., Vigmond, E. J., Bernus, O., and Walton, R. D., A comprehensive framework for evaluation of high pacing frequency and arrhythmic optical mapping signals. Front. Physiol. 2023;14: doi:10.3389/fphys.2023.734356.
24. Ricci, E., Bartolucci, C., and Severi, S., The virtual sinoatrial node: What did computational models tell us about cardiac pacemaking? Progress in Biophysics and Molecular Biology 2023;177:55–79. doi:10.1016/j.pbiomolbio.2022.10.008.
25. Rinné, S., Oertli, A., Nagel, C., Tomsits, P., Jenewein, T., Kääb, S., Kauferstein, S., Loewe, A., Beckmann, B. M., and Decher, N., Functional characterization of a spectrum of novel romano-ward syndrome KCNQ1 variants. IJMS 2023;24:1350. doi:10.3390/ijms24021350.
26. Schwarz, E. L., Pegolotti, L., Pfaller, M. R., and Marsden, A. L., Beyond CFD: Emerging methodologies for predictive simulation in cardiovascular health and disease. Biophysics Reviews 2023;4: doi:10.1063/5.0109400.
27. Correas, M., Guillem, M. S., and Sánchez, J., “Automated generation of purkinje networks in the human heart considering the anatomical variability,” 2023 in Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 127–136. doi:10.1007/978-3-031-35302-4_13.
28. Ghebryal, J., Rodero, C., Barrows, R. K., Strocchi, M., Roney, C. H., Augustin, C. M., Plank, G., and Niederer, S. A., “Effect of varying pericardial boundary conditions on whole heart function: A computational study,” 2023 in Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 545–554. doi:10.1007/978-3-031-35302-4_56.
29. Jost, T. T., Thangamani, A., Colin, R., Loechner, V., Genaud, S., and Bramas, B., GPU code generation of cardiac electrophysiology simulation with MLIR,” 2023 in Euro-Par 2023: Parallel Processing (Springer Nature Switzerland), 549–563. doi:10.1007/978-3-031-39698-4_37.
30. Maier, B., Göddeke, D., Huber, F., Klotz, T., Röhrle, O., and Schulte, M., OpenDiHu: An efficient and scalable framework for biophysical simulations of the neuromuscular system. 2023; doi:10.2139/ssrn.4474485.
31. Pigozzo, R. B., Santos, R. W. dos, and Rocha, B. M., “Sensitivity analysis of a cardiac electrophysiology model for the occurrence of electrical alternans,” 2023 in Computational Science and Its Applications ICCSA 2023 Workshops (Springer Nature Switzerland), 44–58. doi:10.1007/978-3-031-37105-9_4.
32. Romitti, G. S., Liberos, A., Romero, P., Serra, D., Garcı́a, I., Lozano, M., Sebastian, R., and Rodrigo, M., “Cellular automata for fast simulations of arrhythmogenic atrial substrate,” 2023 in Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 107–116. doi:10.1007/978-3-031-35302-4_11.
33. Solis-Lemus, J. A., Baptiste, T., Barrows, R., Sillett, C., Gharaviri, A., Raffaele, G., Razeghi, O., Strocchi, M., Sim, I., Kotadia, I., Bodagh, N., O’Hare, D., O’Neill, M., Williams, S. E., et al., Model reproducibility study on left atrial fibres. 2023; doi:10.48550/ARXIV.2301.06998.
34. Steyer, J., Diaz, L. P. M., Unger, L. A., and Loewe, A., “Simulated excitation patterns in the atria and their corresponding electrograms,” 2023 in Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 204–212. doi:10.1007/978-3-031-35302-4_21.
35. Tolkacheva, E., Zhao, X., and Dierckx, H. eds., Nonlinear analysis and machine learning in cardiology. Frontiers Media SA 2023 doi:10.3389/978-2-8325-2293-6.
36. Willems, R., Kruithof, E., Janssens, K. L. P. M., Cluitmans, M. J. M., Sluis, O. van der, Bovendeerd, P. H. M., and Verhoosel, C. V., “Isogeometric-mechanics-driven electrophysiology simulations of ventricular tachycardia,” 2023 in Functional Imaging and Modeling of the Heart (Springer Nature Switzerland), 97–106. doi:10.1007/978-3-031-35302-4_10.
37. Alberto Barrios Espinosa, "Cristian, Sánchez, J., Doessel, O., and Loewe", A., Diffusion reaction eikonal alternant model: Towards fast simulations of complex cardiac arrhythmias. 2022 in Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2022.054.
38. Gillette, "Karli, Gsell, M., Kurath-Koller, S., Manninger, M., Prassl, A. J., Scherr, D., and Plank", G., Exploring role of accessory pathway location in wolff-parkinson-white syndrome in a model of whole heart electrophysiology. 2022 in Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2022.057.
39. Martinez Diaz, "Patricia, Sánchez, J., Nagel, C., Perez, M. M., Hernández-Romero, I., Salud Guillem Sánchez, M. de la, Doessel, O., and Loewe", A., Personalized modeling of atrial activation and p-waves: A comparison between invasive and non-invasive cardiac mapping. 2022 in Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2022.334.
40. Sira Romitti, "Giada, Antonio, P. R. de, Mascarell, A. L., Almor, D. S., Fernandez, I. G., Ibañez, M. L., Aguilar, R. S., and Bort", M. R., Effects of long- and short-term memory on action potential duration for atrial cellular automata. 2022 in Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2022.258.
41. Uv, J. J., and Arevalo, H., Electrophysiological simulation of maternal-fetal ECG on a 3D maternal torso model. 2022 in Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2022.136.
42. Lei, C. L., Clerx, M., Gavaghan, D. J., and Mirams, G. R., Model-driven optimal experimental design for calibrating cardiac electrophysiology models. 2022; doi:10.1101/2022.11.01.514669.
43. Rappel, W.-J., The physics of heart rhythm disorders. 2022; doi:10.52843/cassyni.598vpx.
44. Rodero, C., Longobardi, S., Augustin, C., Strocchi, M., Plank, G., Lamata, P., and Niederer, S. A., Calibration of cohorts of virtual patient heart models using bayesian history matching. Ann Biomed Eng 2022;51:241–252. doi:10.1007/s10439-022-03095-9.
45. Zhu, C., Vedula, V., Parker, D., Wilson, N., Shadden, S., and Marsden, A., svFSI: A multiphysics package for integrated cardiac modeling. JOSS 2022;7:4118. doi:10.21105/joss.04118.
46. Lange, M., Kwan, E., Dosdall, D. J., MacLeod, R. S., Bunch, T. J., and Ranjan, R., Case report: Personalized computational model guided ablation for left atrial flutter. Front. Cardiovasc. Med. 2022;9: doi:10.3389/fcvm.2022.893752.
47. Azzolin, L., Eichenlaub, M., Nagel, C., Nairn, D., Sanchez, J., Unger, L., Dössel, O., Jadidi, A., and Loewe, A., Personalized ablation vs. Conventional ablation strategies to terminate atrial fibrillation and prevent recurrence. EP Europace 2022;25:211–222. doi:10.1093/europace/euac116.
48. Campos, F. O., Neic, A., Costa, C. M., Whitaker, J., O’Neill, M., Razavi, R., Rinaldi, C. A., DanielScherr, Niederer, S. A., Plank, G., and Bishop, M. J., An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Medical Image Analysis 2022;80:102483. doi:10.1016/
49. Skupien, N., Espinosa, C. B., 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.
50. Coveney, S., Cantwell, C., and Roney, C., Atrial conduction velocity mapping: Clinical tools, algorithms and approaches for understanding the arrhythmogenic substrate. Med Biol Eng Comput, Biological Engineering, Computing 2022;60:2463–2478. doi:10.1007/s11517-022-02621-0.
51. Goette, A., Rickert, V., and Brandner, S., Simulatoren und simulatortraining in der interventionellen elektrophysiologie. Herzschr Elektrophys 2022;33:351–354. doi:10.1007/s00399-022-00882-8.
52. Amsaleg, A., Sánchez, J., Mikut, R., and Loewe, A., Characterization of the pace-and-drive capacity of the human sinoatrial node: A 3D in silico study. 2022; doi:10.1101/2022.06.03.494644.
53. Esmailie, F., Razavi, A., Yeats, B., Sivakumar, S. K., Chen, H., Samaee, M., Shah, I. A., Veneziani, A., Yadav, P., Thourani, V. H., and Dasi, L. P., Biomechanics of transcatheter aortic valve replacement complications and computational predictive modeling. Structural Heart 2022;6:100032. doi:10.1016/j.shj.2022.100032.
54. Hustad, K. G., and Cai, X., Resource-efficient use of modern processor architectures for numerically solving cardiac ionic cell models. Front. Physiol. 2022;13: doi:10.3389/fphys.2022.904648.
55. Rodrı́guez-Padilla, J., Petras, A., Magat, J., Bayer, J., Bihan-Poudec, Y., Hamrani, D. E., Ramlugun, G., Neic, A., Augustin, C. M., Vaillant, F., Constantin, M., Benoist, D., Pourtau, L., Dubes, V., et al., Impact of intraventricular septal fiber orientation on cardiac electromechanical function. American Journal of Physiology-Heart and Circulatory Physiology 2022;322:H936–H952. doi:10.1152/ajpheart.00050.2022.
56. Karabelas, E., Gsell, M. A. F., Haase, G., Plank, G., and Augustin, C. M., An accurate, robust, and efficient finite element framework with applications to anisotropic, nearly and fully incompressible elasticity. Computer Methods in Applied Mechanics and Engineering 2022;394:114887. doi:10.1016/j.cma.2022.114887.
57. Sánchez, J., and Loewe, A., A review of healthy and fibrotic myocardium microstructure modeling and corresponding intracardiac electrograms. Front. Physiol. 2022a;13: doi:10.3389/fphys.2022.908069.
58. Geng, Z., Jin, L., Huang, Y., and Wu, X., Rate dependence of early afterdepolarizations in the his-purkinje system: A simulation study. Computer Methods and Programs in Biomedicine 2022;217:106665. doi:10.1016/j.cmpb.2022.106665.
59. Serra, D., Romero, P., Garcia-Fernandez, I., Lozano, M., Liberos, A., Rodrigo, M., Bueno-Orovio, A., Berruezo, A., and Sebastian, R., An automata-based cardiac electrophysiology simulator to assess arrhythmia inducibility. Mathematics 2022;10:1293. doi:10.3390/math10081293.
60. Jung, A., Gsell, M. A. F., Augustin, C. M., and Plank, G., An integrated workflow for building digital twins of cardiac electromechanicsa multi-fidelity approach for personalising active mechanics. Mathematics 2022;10:823. doi:10.3390/math10050823.
61. 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.
62. Blackwell, D. J., Faggioni, M., Wleklinski, M. J., Gomez-Hurtado, N., Venkataraman, R., Gibbs, C. E., Baudenbacher, F. J., Gong, S., Fishman, G. I., Boyle, P. M., Pfeifer, K., and Knollmann, B. C., The purkinjemyocardial junction is the anatomic origin of ventricular arrhythmia in CPVT. JCI Insight 2022;7: doi:10.1172/jci.insight.151893.
63. Fuchsberger, J., Aigner, P., Niederer, S., Plank, G., Schima, H., Haase, G., and Karabelas, E., On the incorporation of obstacles in a fluid flow problem using a navierstokesbrinkman penalization approach. Journal of Computational Science 2022;57:101506. doi:10.1016/j.jocs.2021.101506.
64. Alba, V., Load balancing and precision analysis for cardiac simulation M2 Internship report. 2022; Available at:
65. Bach, F., Klar, J., Loewe, A., Sánchez, J., Seemann, G., Huang, Y.-L., and Ulrich, R., The openCARP CDE: Concept for and implementation of a sustainable collaborativedevelopment environment for research software. Bausteine Forschungsdatenmanagement 2022;2022:64–84. doi:10.17192/bfdm.2022.1.8368.
66. Franco Ocaña, P., Electrophysiological model of the left ventricle: Prediction of reentry circuits with fast simulations based on cellular automata applying clinical stimulation protocols. 2022;
67. Klein, V. S., Modeling and measuring cardiac magnetostimulation. 2022;
68. Lee, C. H., Application of neural networks to predict patient-specific cellular parameters in computational cardiac models. 2022;
69. Loewe, A., Dı́az, P. M., Nagel, C., and Sánchez, J., “Cardiac digital twin modeling,” 2022 in Innovative Treatment Strategies for Clinical Electrophysiology (Springer Nature Singapore), 111–134. doi:10.1007/978-981-19-6649-1_7.
70. Reimer, J. A., A comparison of the bidomain and EMI models in refractory cardiac tissue. 2022; Available at:
71. Sánchez, J., and Loewe, A., Mechanical consequences of electrical remodeling due to persistent atrial fibrillation: A cellular level sensitivity analysis. 2022b in 49th Computing in Cardiology Conference (CinC).
72. Nagel, C., Schuler, S., Dössel, O., and Loewe, A., A bi-atrial statistical shape model for large-scale in silico studies of human atria: Model development and application to ECG simulations. Medical Image Analysis 2021;74:102210. doi:10.1016/
73. Tong, L., Zhao, C., Fu, Z., Dong, R., Wu, Z., Wang, Z., Zhang, N., Wang, X., Cao, B., Sun, Y., Zheng, D., Xia, L., and Deng, D., Preliminary study: Learning the impact of simulation time on reentry location and morphology induced by personalized cardiac modeling. Front. Physiol. 2021;12: doi:10.3389/fphys.2021.733500.
74. Maleckar, M. M., Myklebust, L., Uv, J., Florvaag, P. M., Strøm, V., Glinge, C., Jabbari, R., Vejlstrup, N., Engstrøm, T., Ahtarovski, K., Jespersen, T., Tfelt-Hansen, J., Naumova, V., and Arevalo, H., Combined in-silico and machine learning approaches toward predicting arrhythmic risk in post-infarction patients. Front. Physiol. 2021;12: doi:10.3389/fphys.2021.745349.
75. 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 2021;10:2852. doi:10.3390/cells10112852.
76. 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.
77. Azzolin, L., Nagel, C., Nairn, D., Sanchez, J., Zheng, T., Eichenlaub, M., Jadidi, A., Dossel, O., and Loewe, A., Automated framework for the augmentation of missing anatomical structures and generation of personalized atrial models from clinical data. 2021 in 2021 Computing in Cardiology (CinC) (IEEE). doi:10.23919/cinc53138.2021.9662846.
78. Ryzhii, M., and Ryzhii, E., Pacemaking function of two simplified cell models. 2021; doi:10.1101/2021.09.14.460406.
79. 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. Front. Physiol. 2021;12: doi:10.3389/fphys.2021.693015.
80. Dössel, O., Luongo, G., Nagel, C., and Loewe, A., Computer modeling of the heart for ECG interpretationa review. Hearts 2021;2:350–368. doi:10.3390/hearts2030028.
81. 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.
82. Luongo, G., Azzolin, L., Schuler, S., Rivolta, M. W., Almeida, T. P., Martı́nez, 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.
83. 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.
84. 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.
85. 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.
86. Monbaliu, R., A meandering spiral due to early afterdepolarizations as possible mechanism of torsade de pointes. 2021;
87. Moreno, A., Walton, R. D., Bernus, O., Vigmond, E. J., and Bayer, J. D., Low-energy, single-pulse surface stimulation defibrillates large mammalian ventricles. Heart Rhythm 2021; doi:10.1016/j.hrthm.2021.10.006.
88. 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.
89. Wleklinski, M. J., Kannankeril, P. J., and Knollmann, B. C., Molecular and tissue mechanisms of catecholaminergic polymorphic ventricular tachycardia. The Journal of Physiology 2020;598:2817–2834. doi:10.1113/jp276757.
90. Azzolin, L., Luongo, G., Rocher, S., Saiz, J., Doessel, O., and Loewe, A., Influence of gradient and smoothness of atrial wall thickness on initiation and maintenance of atrial fibrillation. 2020 in 47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2020.261.
91. Luongo, G., Azzolin, L., Rivolta, M. W., Almeida, T. P. de, Martı́nez, J. P., Soriano, D. C., Doessel, O., Sassi, R., Laguna, P., and Loewe, A., Machine learning to find areas of rotors sustaining atrial fibrillation from the ECG. 2020a in 47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2020.181.
92. Luongo, G., Azzolin, L., Rivolta, M. W., Sassi, R., Martinez, J. P., Laguna, P., Dossel, O., and Loewe, A., Non-invasive identification of atrial fibrillation driver location using the 12-lead ECG: Pulmonary vein rotors vs. Other locations. 2020b in 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) (IEEE). doi:10.1109/embc44109.2020.9176135.
93. Sánchez, J., Nothstein, M., Neic, A., Huang, Y.-L., Prassl, A. J., Klar, J., Ulrich, R., Bach, F., Zschumme, P., Selzer, M., Plank, G., Vigmond, E., Seemann, G., and Loewe, A., openCARP: An open sustainable framework for in-silico cardiac electrophysiology research. 2020 in 47th Computing in Cardiology Conference (CinC) (Computing in Cardiology). doi:10.22489/cinc.2020.111.
94. Camargo, M. L. A., Bassani, J. W. M., Bassani, R. A., and Silva, R. R., ForceLAB simulator update: A study on the positive cooperativity of cross-bridge formation in ventricular myocyte. EasyChair, 2022;
95. Ogbomo-Harmitt, S., Qureshi, A., King, A., and Aslanidi, O., Impact of fibrosis border zone characterisation on fibrosis-substrate isolation ablation outcome for atrial fibrillation. NA; Available at:
96. Arciniegas, J. P. S., A multiscale in silico study to characterize the atrial electrical activity of patients with atrial fibrillation. A translational study to guide ablation therapy. doi:10.4995/thesis/10251/171456.
97. Cloet, M., Volkaerts, M., Samaey, G., Claus, P., and Dierckx, H., Modeling cardiac tissue at multiple scales for bayesian inversion. Available at:

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).

© Copyright 2020 openCARP project    Supported by DFG and EuroHPC    Contact    Imprint and data protection