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Towards personalized clinical in-silico modeling of atrial anatomy and electrophysiology

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Abstract

Computational atrial models aid the understanding of pathological mechanisms and therapeutic measures in basic research. The use of biophysical models in a clinical environment requires methods to personalize the anatomy and electrophysiology (EP). Strategies for the automation of model generation and for evaluation are needed. In this manuscript, the current efforts of clinical atrial modeling in the euHeart project are summarized within the context of recent publications in this field. Model-based segmentation methods allow for the automatic generation of ready-to-simulate patient-specific anatomical models. EP models can be adapted to patient groups based on a-priori knowledge and to the individual without significant further data acquisition. ECG and intracardiac data build the basis for excitation personalization. Information from late enhancement (LE) MRI can be used to evaluate the success of radio-frequency ablation (RFA) procedures and interactive virtual atria pave the way for RFA planning. Atrial modeling is currently in a transition from the sole use in basic research to future clinical applications. The proposed methods build the framework for model-based diagnosis and therapy evaluation and planning. Complex models allow to understand biophysical mechanisms and enable the development of simplified models for clinical applications.

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References

  1. Akoum N, Daccarett M, McGann C, et al (2011) Atrial fibrosis helps select the appropriate patient and strategy in catheter ablation of atrial fibrillation: a de-mri guided approach. J Cardiovasc Electrophysiol 22:16–22

    Article  PubMed  Google Scholar 

  2. Ashihara T, Haraguchi R, Nakazawa K et al (2012) The role of fibroblasts in complex fractionated electrograms during persistent/permanent atrial fibrillation implications for electrogram-based catheter ablation. Circ Res 110:275–284

    Google Scholar 

  3. Aslanidi OV, Colman MA, Stott J et al (2011) 3d virtual human atria: a computational platform for studying clinical atrial fibrillation. Progress Biophys Mol Biol 107:156–168

    Article  Google Scholar 

  4. Atienza F, Almendral J, Jalife J et al (2009) Real-time dominant frequency mapping and ablation of dominant frequency sites in atrial fibrillation with left-to-right frequency gradients predicts long-term maintenance of sinus rhythm. Heart Rhythm 6:33–40

    Article  PubMed  Google Scholar 

  5. Atienza F, Almendral J, Moreno J et al (2006) Activation of inward rectifier potassium channels accelerates atrial fibrillation in humans: evidence for a reentrant mechanism. Circulation 114:2434–2442

    Article  PubMed  CAS  Google Scholar 

  6. Atienza F, Calvo D, Almendral J et al (2011) Mechanisms of fractionated electrograms formation in the posterior left atrium during paroxysmal atrial fibrillation in humans. J Am Coll Cardiol 57:1081–1092

    Article  PubMed  Google Scholar 

  7. Berjano EJ (2006) Theoretical modeling for radiofrequency ablation: state-of-the-art and challenges for the future. Biomed Eng Online 5:24

    Article  PubMed  Google Scholar 

  8. Burdumy M, Luik A, Neher P et al (2012) Comparing measured and simulated wave directions in the left atrium—a workflow for model personalization and validation. Biomed Tech (Berl) 57:79–87

    Article  Google Scholar 

  9. Cabrera JA, Ho SY, Climent V et al (2008) The architecture of the left lateral atrial wall: a particular anatomic region with implications for ablation of atrial fibrillation. Eur Heart J 29:356–362

    Article  PubMed  Google Scholar 

  10. Calkins H, Kuck KH, Cappato R et al (2012) 2012 hrs/ehra/ecas expert consensus statement on catheter and surgical ablation of atrial fibrillation: recommendations for patient selection, procedural techniques, patient management and follow-up, definitions, endpoints, and research trial design. J Interv Cardiac Electrophysiol 33:171–257

    Article  Google Scholar 

  11. Courtemanche M, Ramirez RJ, Nattel S (1998) Ionic mechanisms underlying human atrial action potential properties: Insights from a mathematical model. Am J Physiol 275:H301–H321

    PubMed  CAS  Google Scholar 

  12. Cuculich PS, Wang Y, Lindsay BD et al (2010) Noninvasive characterization of epicardial activation in humans with diverse atrial fibrillation patternsclinical perspective. Circulation 122:1364–1372

    Article  PubMed  Google Scholar 

  13. van Dam PM, van Oosterom A (2003) Atrial excitation assuming uniform propagation. J Cardiovasc Electrophysiol 14:S166–71

    Article  PubMed  Google Scholar 

  14. Dang L, Virag N, Ihara Z et al (2005) Evaluation of ablation patterns using a biophysical model of atrial fibrillation. Annals Biomed Eng 33:465–474

    Article  CAS  Google Scholar 

  15. Doll N, Pritzwald-Stegmann P, Czesla M et al (2008) Ablation of ganglionic plexi during combined surgery for atrial fibrillation. Annals Thoracic Surg 86:1659–1663

    Article  Google Scholar 

  16. D ö ssel O, Krueger MW, Weber FM et al (2011) A framework for personalization of computational models of the human atria. Conf Proc IEEE Eng Med Biol Soc 2011:4324–4328

    Google Scholar 

  17. Dössel O, Krueger MW, Weber FM et al (2012) Computational modeling of the human atrial anatomy and electrophysiology. Med Biol Eng Comput 50(8):773–799

    Google Scholar 

  18. Ecabert O, Peters J, Schramm H et al (2008) Automatic model-based segmentation of the heart in ct images. IEEE Trans Med Imag 27:1189–1201

    Article  Google Scholar 

  19. Fischer G, Pfeifer B, Seger M et al (2005) Computationally efficient noninvasive cardiac activation time imaging. Methods Inform Med 44:674

    CAS  Google Scholar 

  20. Fonseca CG, Backhaus M, Bluemke DA et al (2011) The cardiac atlas project?an imaging database for computational modeling and statistical atlases of the heart. Bioinformatics 27:2288–2295

    Article  PubMed  CAS  Google Scholar 

  21. Fuster V, Ryden LE, Cannom DS et al (2006) Acc/aha/esc 2006 guidelines for the management of patients with atrial fibrillation: a report of the american college of cardiology/american heart association task force on practice guidelines and the european society of cardiology committee for practice guidelines (writing committee to revise the 2001 guidelines for the management of patients with atrial fibrillation): developed in collaboration with the european heart rhythm association and the heart rhythm society

  22. Gianni D, McKeever S, Yu T et al (2010) Sharing and reusing cardiovascular anatomical models over the web: a step towards the implementation of the virtual physiological human project. Philos Trans. Series A Math Phys Eng Sci 368:3039–3056

    Article  Google Scholar 

  23. Grandi E, Pandit SV, Voigt N et al (2011) Human atrial action potential and Ca2+ model: Sinus rhythm and chronic atrial fibrillation. Circ Res 109:1055–1066

    Article  PubMed  CAS  Google Scholar 

  24. Guillem MS, Climent AM, Castells F et al (2009) Noninvasive mapping of human atrial fibrillation. J Cardiovasc Electrophysiol 20:507–513

    Article  PubMed  Google Scholar 

  25. Haissaguerre M, Jaïs P, Shah DC et al (1998) Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. New Engl J Med 339:659–666

    Article  PubMed  CAS  Google Scholar 

  26. Hanna R, Barschdorf H, Klinder T et al (2011) A hybrid method for automatic anatomical variant detection and segmentation. Functional imaging and modeling of the heart 2011. Lect Notes Comput Sci 6666:333–340

    Google Scholar 

  27. Ho SY, Sanchez-Quintana D (2009) The importance of atrial structure and fibers. Clin Anat 22:52–63

    Article  PubMed  CAS  Google Scholar 

  28. Holmqvist F, Husser D, Tapanainen JM et al (2008) Interatrial conduction can be accurately determined using standard 12-lead electrocardiography: validation of P-wave morphology using electroanatomic mapping in man. Heart Rhythm 5:413–418

    Article  PubMed  Google Scholar 

  29. Huiskamp G, Greensite F (1997) A new method for myocardial activation imaging. IEEE Trans Biomed Eng 44:433–446

    Article  PubMed  CAS  Google Scholar 

  30. Jacquemet V (2011) An eikonal-diffusion solver and its application to the interpolation and the simulation of reentrant cardiac activations. Comput Methods Programs Biomed 108(2):548–558

    Google Scholar 

  31. Jacquemet V, Kappenberger L, Henriquez CS (2008) Modeling atrial arrhythmias: impact on clinical diagnosis and therapies. IEEE Rev Biomed Eng 1:94–114

    Article  PubMed  Google Scholar 

  32. Jacquemet V, Virag N, Ihara Z et al (2003) Study of unipolar electrogram morphology in a computer model of atrial fibrillation. J Cardiovasc Electrophysiol 14:S172–9

    Article  PubMed  Google Scholar 

  33. Keller DUJ, Weber FM, Seemann G et al (2010) Ranking the influence of tissue conductivities on forward-calculated ecgs. IEEE Trans Biomed Eng 57:1568–1576

    Article  PubMed  Google Scholar 

  34. Knowles BR, Caulfield D, Cooklin M et al (2010) 3-d visualization of acute rf ablation lesions using mri for the simultaneous determination of the patterns of necrosis and edema. IEEE Trans Biomed Eng 57:1467–1475

    Article  PubMed  Google Scholar 

  35. Koivumaeki JT, Korhonen T, Tavi P (2011) Impact of sarcoplasmic reticulum calcium release on calcium dynamics and action potential morphology in human atrial myocytes: a computational study. PLoS Comput Biol 7:e1001067

    Google Scholar 

  36. Krueger MW, Rhode K, Weber FM et al (2010) Patient-specific volumetric atrial models with electrophysiological components: a comparison of simulations and measurements. Biomedizinische Technik / Biomed Eng 55(s1):54–57

    Google Scholar 

  37. Krueger MW, Schmidt V, Tobón C et al (2011) Modeling atrial fiber orientation in patient-specific geometries: a semi-automatic rule-based approach. In: Axel L, Metaxas D (eds) Functional imaging and modeling of the heart 2011. Lect Notes Comput Sci 6666:223–232

  38. Krueger MW, Seemann G, Rhode K et al (2012) Personalization of atrial anatomy and elelectophysiology as a basis for clinical modeling of radio-frequency-ablation of atrial fibrillation. IEEE Trans Med Imag. doi:10.1109/TMI.2012.2201948

  39. Krueger MW, Severi S, Rhode K et al (2011) Alterations of atrial electrophysiology related to hemodialysis session: insights from a multiscale computer model. J Electrocardiol 44:176–183

    Article  PubMed  Google Scholar 

  40. Krueger MW, Weber FM, Seemann G et al (2009) Semi-automatic segmentation of sinus node, Bachmann’s bundle and terminal crest for patient specific atrial models. In: World Congress on Medical Physics and Biomedical Engineering. IFMBE Proceedings. Springer, Heidelberg, vol 25/4, pp 673–676

  41. Lazar S, Dixit S, Marchlinski FE et al (2004) Presence of left-to-right atrial frequency gradient in paroxysmal but not persistent atrial fibrillation in humans. Circulation 110:3181–3186

    Article  PubMed  Google Scholar 

  42. Lloyd CM, Halstead MDB, Nielsen PF (2004) CellML: its future, present and past. Progress Biophys Mol Biol 85:433–450

    Article  CAS  Google Scholar 

  43. Lu W, Zhu X, Chen W et al (2011) A computer model based on real anatomy for electrophysiology study. Adv Eng Softw 42:463–476

    Article  Google Scholar 

  44. Maleckar MM, Greenstein JL, Giles WR et al (2009) Electrotonic coupling between human atrial myocytes and fibroblasts alters myocyte excitability and repolarization. Biophys J 97:2179–2190

    Article  PubMed  CAS  Google Scholar 

  45. Maleckar MM, Greenstein JL, Giles WR et al (2009) K+ current changes account for the rate dependence of the action potential in the human atrial myocyte. Am J Physiol Heart Circ Physiol 297:H1398–410

    Article  PubMed  CAS  Google Scholar 

  46. Marom EM, Herndon JE, Kim YH et al (2004) Variations in pulmonary venous drainage to the left atrium: implications for radiofrequency ablation. Radiology 230:824–829

    Article  PubMed  Google Scholar 

  47. Mihalef V, Ionasec RI, Sharma P et al (2011) Patient-specific modelling of whole heart anatomy, dynamics and haemodynamics from four-dimensional cardiac ct images. Interface Focus 1:286–296

    Article  PubMed  Google Scholar 

  48. Modre R, Tilg B, Fischer G, Hanser F, Messnarz B, Seger M, Schocke MFH, Berger T, Hintringer F, Roithinger FX (2003) Atrial noninvasive activation mapping of paced rhythm data. J Cardiovasc Electrophysiol 14:712–719

    Article  PubMed  Google Scholar 

  49. Neher P, Barschdorf H, Dries S et al (2011) Automatic segmentation of cardiac CTs—personalized atrial models augmented with electrophysiological structures. In: Functional imaging and modeling of the heart 2011. Lect Notes Comput Sci 6666:80–87

    Article  Google Scholar 

  50. Nygren A, Fiset C, Firek L et al (1998) Mathematical model of a adult human atrial cell. the role of K+ currents in repolarization. Circ Res 82:63–81

    Article  PubMed  CAS  Google Scholar 

  51. van Oosterom A, Jacquemet V (2005) Genesis of the P wave: atrial signals as generated by the equivalent double layer source model. Europace 2(7):21–29

    Article  Google Scholar 

  52. Plank G, Prassl AJ, Wang JI et al (2008) Atrial fibrosis promotes the transistion of pulmonary vein ectopy into reentrant arrhathmias. Heart Rhythm 5:S162–S163

    Google Scholar 

  53. Platonov PG, Ivanov V, Ho SY et al (2008) Left atrial posterior wall thickness in patients with and without atrial fibrillation: data from 298 consecutive autopsies. J Cardiovasc Electrophysiol 19:689–692

    Article  PubMed  Google Scholar 

  54. Platonov PG, Mitrofanova L, Ivanov V et al (2008) Substrates for intra-atrial and interatrial conduction in the atrial septum: anatomical study on 84 human hearts. Heart Rhythm Off J Heart Rhythm Soc 5:1189–1195

    Article  Google Scholar 

  55. Ponto S, Schilling C, Krueger MW et al (2011) Influence of endocardial catheter contact on properties of the atrial signal and comparison with simulated electrograms. Biomedizinische Technik / Biomed Eng (Proc. BMT 2011) 56

  56. Reumann M, Bohnert J, Seemann G et al (2008) Preventive ablation strategies in a biophysical model of atrial fibrillation based on realistic anatomical data. IEEE Trans Biomed Eng 55:399–406

    Article  PubMed  Google Scholar 

  57. Richter U, Faes L, Cristoforetti A et al (2011) A novel approach to propagation pattern analysis in intracardiac atrial fibrillation signals. Annals Biomed Eng 39:310–323

    Article  Google Scholar 

  58. Ridler ME, Lee M, McQueen D et al (2011) Arrhythmogenic consequences of action potential duration gradients in the atria. Can J Cardiol 27:112–119

    Article  PubMed  Google Scholar 

  59. Sanders P, Berenfeld O, Hocini M et al (2005) Spectral analysis identifies sites of high-frequency activity maintaining atrial fibrillation in humans. Circulation 112:789–797

    Article  PubMed  Google Scholar 

  60. Saremi F, Krishnan S (2007) Cardiac conduction system: anatomic landmarks relevant to interventional electrophysiologic techniques demonstrated with 64-detector CT. Radiographics Rev 27:1539–1565

    Article  Google Scholar 

  61. Schotten U, Verheule S, Kirchhof P et al (2011) Pathophysiological mechanisms of atrial fibrillation: a translational appraisal. Physiol Rev 91:265–325

    Article  PubMed  Google Scholar 

  62. Schulze WHW, Krueger MW, Jiang Y et al (2010) Localization of the atrial excitation origin by reconstruction of time-integrated transmembrane voltages. Biomedizinische Technik/Biomed Eng 55:103–106

    Google Scholar 

  63. Schulze WHW, Krueger MW, Rhode K et al (2011) Critical times based activation time imaging. In: Proceedings of the 38th International Congress on Electrocardiology

  64. Seemann G, Carillo P, Weiss DL et al (2009) Investigating arrhythmogenic effects of the herg mutation n588k in virtual human atria. Lecture Notes in Computer Science, vol. 5528, pp 144–153

  65. Seemann G, Carrillo Bustamante P, Ponto S et al (2010) Atrial fibrillation-based electrical remodeling in a computer model of the human atrium. Proc Comput Cardiol 37:417–420

    Google Scholar 

  66. Seemann G, Höper C, Sachse FB et al (2006) Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Phil Trans Roy Soc A 364:1465–1481

    Article  CAS  Google Scholar 

  67. Seemann G, Keller DUJ, Krueger MW et al (2010) Electrophysiological modeling for cardiology: methods and potential applications. Inform Technol 52:242–249

    Google Scholar 

  68. Sermesant M, Konukoglu E, Delingette H et al (2007) Functional imaging and modeling of the heart. An Anisotropic multi-front fast-marching method for real-time simulation of cardiac electrophysiology, vol. 4466. Springer, Berlin/Heidelberg, pp 160–169

  69. Smith N, de Vecchi A, McCormick M et al (2011) euheart: personalized and integrated cardiac care using patient-specific cardiovascular modelling. Interface Focus 1(3):349–364

    Google Scholar 

  70. Tobon C, Ruiz C, Rodriguez JF et al (2010) A biophysical model of atrial fibrillation to simulate the Maze III ablation pattern. Proc Comput Cardiol 37:621–624

    Google Scholar 

  71. Weber FM, Luik A, Schilling C et al (2011) Conduction velocity restitution of the human atrium—an efficient measurement protocol for clinical electrophysiological studies. IEEE Trans Biomed Eng 58:2648–2655

    Article  PubMed  Google Scholar 

  72. Weber FM, Lurz S, Keller DUJ et al (2008) Adaptation of a minimal four-state cell model for reproducing atrial excitation properties. Proceedings of Computer in Cardiology, pp 61–64 (IEEE, Bologna, 14–17 Sept. 2008)

  73. Weber FM, Schilling C, Seemann G et al (2010) Wave-direction and conduction-velocity analysis from intracardiac electrograms—a single-shot technique. IEEE Trans Biomed Eng 57:2394–2401

    Article  PubMed  Google Scholar 

  74. Weber FM, Schilling C, Straub D et al (2009) Extracting clinically relevant circular mapping and coronary sinus catheter potentials from atrial simulations. Lect Notes Comput Sci 5528:30–38

    Article  Google Scholar 

  75. Weese J, Peters J, Waechter I et al (2010) The generation of patient-specific heart models for diagnosis and interventions. Lect Notes Comput Sci 6364:25–35

    Article  Google Scholar 

  76. Wood MA, Fuller IA (2002) Acute and chronic electrophysiologic changes surrounding radiofrequency lesions. J Cardiovasc Electrophysiol 13:56–61

    Article  PubMed  Google Scholar 

  77. Zlochiver S, Yamazaki M, Kalifa J et al (2008) Rotor meandering contributes to irregularity in electrograms during atrial fibrillation. Heart Rhythm Off J Heart Rhythm Soc 5:846–854

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Biomedical Engineering, Karlsruhe Institute of Technology (KIT), Kaiserstr. 12, 76131, Karlsruhe, Germany

    Martin W. Krueger, Walther H. W. Schulze, Gunnar Seemann & Olaf Dössel

  2. Division of Imaging Sciences and Biomedical Engineering, King’s College London, London, UK

    Kawal S. Rhode & Reza Razavi

Authors
  1. Martin W. Krueger
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  2. Walther H. W. Schulze
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  3. Kawal S. Rhode
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  4. Reza Razavi
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  5. Gunnar Seemann
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  6. Olaf Dössel
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Corresponding author

Correspondence to Martin W. Krueger.

Additional information

The research leading to these results has received funding from the European Communitys Seventh Framework Programme (FP7/2007-2013) under grant agreement No. 224495 (euHeart project).

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Krueger, M.W., Schulze, W.H.W., Rhode, K.S. et al. Towards personalized clinical in-silico modeling of atrial anatomy and electrophysiology. Med Biol Eng Comput 51, 1251–1260 (2013). https://doi.org/10.1007/s11517-012-0970-0

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  • DOI: https://doi.org/10.1007/s11517-012-0970-0

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