Inter-model consistency and complementarity: Learning from ex-vivo imaging and electrophysiological data towards an integrated understanding of cardiac physiology

Progress in Biophysics and Molecular Biology

Volumn 107, Issue 1, 2011, Pages 122-133

  Camara, O ^a,b   Sermesant, M ^c,d   Lamata, P ^d,e   Wang, L ^f   Pop, M ^g   Relan, J ^c   De Craene, M ^a,b   Delingette, H ^c   Liu, H ^f,h   Niederer, S ^d   Pashaei, A ^a,b   Plank, G ⁱ   Romero, D ^a,b   Sebastian, R ^j   Wong, K C L ^c   Zhang, H ^k   Ayache, N ^c   Frangi, A F ^a,b   Shi, P ^f   Smith, N P ^d,e   Wright, G A ^g   more..

^a UNIVERSITAT POMPEU FABRA   (Spain)

^b BIOMATERIALES Y NANOMEDICINA CIBER BBN   (Spain)

^c INRIA   (France)

^d KING'S COLLEGE LONDON   (United Kingdom)

^e UNIVERSITY OF OXFORD   (United Kingdom)

^f ROCHESTER INSTITUTE OF TECHNOLOGY   (United States)

^g UNIVERSITY OF TORONTO   (Canada)

^h ZHEJIANG UNIVERSITY   (China)

ⁱ MEDICAL UNIVERSITY OF GRAZ   (Austria)

^j UNIVERSITY OF VALENCIA   (Spain)

^k CHINESE UNIVERSITY OF HONG KONG   (Hong Kong)

more..

Author keywords

Detailed ionic models; Fast conduction Purkinje system; Heart electrophysiology; Maximum a posteriori estimation; Model integration; Optical mapping data; Parameter personalization; Phenomenological models

Indexed keywords

ANIMAL; ARTICLE; BIOLOGICAL MODEL; BIOPHYSICS; CELL MEMBRANE POTENTIAL; CYTOLOGY; DIFFUSION; ELECTROPHYSIOLOGY; HEART; HISTOLOGY; IN VITRO STUDY; NUCLEAR MAGNETIC RESONANCE IMAGING; PERICARDIUM; PHYSIOLOGY; PURKINJE FIBER; REPRODUCIBILITY; SWINE; SYSTEM ANALYSIS; TIME;

ANIMALS; BIOPHYSICAL PROCESSES; DIFFUSION; ELECTROPHYSIOLOGICAL PROCESSES; HEART; MAGNETIC RESONANCE IMAGING; MEMBRANE POTENTIALS; MODELS, BIOLOGICAL; PERICARDIUM; PURKINJE FIBERS; REPRODUCIBILITY OF RESULTS; SWINE; SYSTEMS INTEGRATION; TIME FACTORS;

SUS;

EID: 84860389662     PISSN: 00796107     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.pbiomolbio.2011.07.007     Document Type: Article

References (54)

1. Akaike H. A new look at the statistical model identification, automatic control. IEEE Trans. 1974, 19:716-723.
2. Aliev R.R., Panfilov A.V. A simple two-variable model of cardiac excitation. Chaos Soliton Fract 1996, 7:293-301.
3. Aliot E., Stevenson W., Almendral-Garrote J. EHRA/HRS expert consensus on catheter ablation of ventricular arrhythmias. Europace 2009, 11:771-817.
4. Bernus O., Wilders R., Zemlin C., Verschelde H., Panfilov A. A computationally efficient electrophysiological model of human ventricular cells. Am. J. Physiol. Heart Circ. Physiol. 2002, 282. H2296.
5. Bishop M., Rodriguez B., Eason J., Whiteley J., Trayanova N., Gavaghan D. Synthesis of voltage-sensitive optical signals: application to panoramic optical mapping. Biophysical J. 2006, 90:2938-2945.
6. Bueno-Orovio A., Cherry E., Fenton F. Minimal model for human ventricular action potentials in tissue. J. Theor. Biol. 2008, 253:544-560.
7. Caldwell B., Trew M., Sands G., Hooks D., LeGrice I., Smaill B. Three distinct directions of intramural activation reveal nonuniform side-to-side electrical coupling of ventricular myocytes. Circ. Arrhythm Electrophysiol. 2009, 2:433-440.
8. Camara O., Pop M., Sermesant M., Smith N., Young A. Statistical Atlases and Computational Models of the Heart 2010, 6364. Springer-Verlag, Berlin, Germany. first ed.
9. Camara, O., Pashaei, A., Sebastian, R., Frangi, A., 2010b. Personalization of fast conduction Purkinje system in Eikonal-based electrophysiological models with optical mapping data, in: International workshop on Statistical Atlases and Computational Models of the Heart and Cardiac Electrophysiological Simulation Challenge (STACOM-CESC'10), LNCS 6364, pp. 281-290.
10. Camara, O., Riccobene, C., Romero, D., Pashaei, A., Sebastian, R., Sukno, F., Butakoff, C., De Craene, M., Omedas, P., Frangi, A.F., Velut, J., Philipot, C., Tomoulin, C., Billet, F., Mansi, T., Sermesant, M., Wallman, M., Rodriguez, B., Lamata, P., Smith, N., Chinchapatnam, P., Duckett, S., Razavi, R., Groth, A., Weese, J., Ecabert, O., 2010c. Patient-specific computational models of the heart for a cardiac resynchronization therapy planning platform, in: 1st International Conference on Virtual Physiological Human (VPH'10).
11. Chinchapatnam P., Rhode K., Ginks M., Rinaldi C., Lambiase P., Razavi R., Arridge S., Sermesant M. Model-based imaging of cardiac apparent conductivity and local conduction velocity for diagnosis and planning of therapy. IEEE Trans. Med. Imaging 2008, 27:1631-1642.
12. Chung, D., Pop, M., Sermesant, M., Wright, G., 2006. Stereo reconstruction of the epicardium for optical fluorescence imaging, in: MICCAI Workshop on Biophotonics for diagnosis and treatment. Institute of Mathematical Modelling (IMM), Technical University of Denmark, Technical Report, pp. 33-40.
13. Clayton R., Bernus O., Cherry E., Dierckx H., Fenton F., Mirabella L., Panfilov A., Sachse F., Seemann G., Zhang H. Models of cardiac tissue electrophysiology: progress, challenges and open questions. Prog. Biophys. Mol. Biol. 2011, 104:22-48.
14. Clayton R., Nash M., Bradley C., Panfilov A., Paterson D., Taggart P. Experiment-model interaction for analysis of epicardial activation during human ventricular fibrillation with global myocardial ischaemia. Prog Biophys Mol Biol. 2011, 107:101-111.
15. Conn A., Gould N., Toint P. Trust Region Methods 2000, SIAM.
16. Durrer D., van Dam R.T., Freud G.E., Janse M.J., Meijler F.L., Arzbaecher R.C. Total excitation of the isolated human heart. Circulation 1970, 41:899-912.
17. Ethier M., Bourgault Y. Semi-implicit time discretization schemes for the bidomain model. SIAM J. Numer. Anal. 2008, 46:2443-2468.
18. Fink M., Niederer S.A., Cherry E.M., Fenton F.H., Koivumki J.T., Seemann G., Thul R., Zhang H., Sachse F.B., Beard D., Crampin E.J., Smith N.P. Cardiac cell modelling: observations from the heart of the cardiac physiome project. Prog. Biophys. Mol. Biol. 2011, 104:2-21. (Cardiac Physiome project: Mathematical and Modelling Foundations).
19. Garny A., Noble D., Kohl P. Dimensionality in cardiac modelling. Prog. Biophys. Mol. Biol. 2005, 87:47-66.
20. Hyatt C., Mironov S., Vetter F., Zemlin C., Pertsov A. Optical action potential upstroke morphology reveals near-surface transmural propagation direction. Circ. Res. 2005, 97:277-284.
21. Julier, S.J., Uhlmann, J.K., 1997. A new extension of the Kalman filter to nonlinear systems, in: International Symposium on Aerospace/Defense Sensing, Simulation, and Controls, pp. 182-193.
22. Keener J., Sneyd J. Mathematical Physiology 1998, Springer-Verlag, New York.
23. Lamata, P., Niederer, S., Barber, D., Nordsletten, D., Lee, J., Hose, R.D., Smith, N., 2010a. Personalization of cubic hermite meshes for efficient biomechanical simulations, in: Med Image Comput Comput Assist Interv (2), pp. 380-387.
24. Lamata, P., Niederer, S., Plank, G., Smith, N., 2010b. Generic conduction parameters for predicting activation waves in customised cardiac electrophysiology models, in: International workshop on Statistical Atlases and Computational Models of the Heart and Cardiac Electrophysiological Simulation Challenge (STACOM-CESC'10), LNCS 6364, pp. 261-269.
25. Liu G. Meshfree Methods 2003, CRC Press, Boca Raton, FL.
26. Mitchell C.C., Schaeffer D.G. A two current model for the dynamics of cardiac membrane. Bull. Math. Biol. 2003, 65:767-793.
27. Niederer S., Mitchell L., Smith N., Plank G. Simulation human cardiac electrophysiology on clinical time-scales. Front. Physiol. 2011, 2:1-7.
28. Niederer S.A., Plank G., Chinchapatnam P., Ginks M., Lamata P., Rhode K.S., Rinaldi C.A., Razavi R., Smith N.P. Length-dependent tension in the failing heart and the efficacy of cardiac resynchronization therapy. Cardiovasc. Res. 2011, 89:336-343.
29. Noble D. A modification of the Hodgkin-Huxley equations applicable to Purkinje fibre action and pace-maker potentials. J. Physiol. 1962, 160:317-352.
30. Pashaei, A., Hoogendoorn, C., Sebastián, R., Romero, D., Camara, O., Frangi, A.F., 2011. Effect of scar development on fast electrophysiological models of the human heart: In-silico study on atlas-based virtual populations, in: International Conference on Functional Imaging and Modeling of the Heart (FIMH'11), LNCS 6666, pp. 427-436.
31. Pop, M., Sermesant, M., Chung, D., Liu, G., McVeigh, E., Crystal, E., Wright, G., 2007. An experimental framework to validate 3D models of cardiac electrophysiology via optical imaging and MRI, in: Proceedings of Functional Imaging and Modeling of the Heart 2007 (FIMH'07), volume 4466 of LNCS, pp. 100-109.
32. Pop M., Sermesant M., Lepiller D., Truong M., McVeigh E., Crystal E., Dick A., Delingette H., Ayache N., Wright G. Fusion of optical imaging and MRI for the evaluation and adjustment of macroscopic models of cardiac electrophysiology: a feasibility study. Med. Image Anal. 2009, 13:370-380.
33. Pron Y., Demolombe S., Bar I., Drouin E., Charpentier F., Escande D. Differential expression of kvlqt1 isoforms across the human ventricular wall. Am. J. Physiol. Heart Circ. Physiol. 2000, 278:H1908-H1915.
34. Quinn T.A., Granite S., Allessie M.A., Antzelevitch C., Bollensdorff C., Bub G., Burton R.A., Cerbai E., Chen P.S., Delmar M., Difrancesco D., Earm Y.E., Efimov I.R., Egger M., Entcheva E., Fink M., Fischmeister R., Franz M.R., Garny A., Giles W.R., Hannes T., Harding S.E., Hunter P.J., Iribe G., Jalife J., Johnson C.R., Kass R.S., Kodama I., Koren G., Lord P., Markhasin V.S., Matsuoka S., McCulloch A.D., Mirams G.R., Morley G.E., Nattel S., Noble D., Olesen S.P., Panfilov A.V., Trayanova N.A., Ravens U., Richard S., Rosenbaum D.S., Rudy Y., Sachs F., Sachse F.B., Saint D.A., Schotten U., Solovyova O., Taggart P., Tung L., Varró A., Volders P.G., Wang K., Weiss J.N., Wettwer E., White E., Wilders R., Winslow R.L., Kohl P. Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): Standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol. 2011, 107:4-10.
35. Relan J., Sermesant M., Delingette H., Pop M., Wright G.A., Ayache N. Quantitative Comparison Of Two Cardiac Electrophysiology Models Using Personalisation To Optical And Mr Data 2009, ISBI, 1027-1030.
36. Relan, J., Pop, M., Delingette, H., Wright, G., Ayache, N., Sermesant, M., in press. Personalisation of a cardiac electrophysiology model using optical mapping and MRI for prediction of changes with pacing, IEEE Trans Biomed Eng.
37. Relan, J., Chinchapatnam, P., Sermesant, M., Rhode, K., Delingette, H., Razavi, R., Ayache, N., 2010a. Coupled personalisation of electrophysiology models for simulation of induced ischemic ventricular tachycardia, in: Proc. Medical Image Computing and Computer Assisted Intervention (MICCAI'10), LNCS, Beijing, China.
38. Relan, J., Pop, M., Delingette, H., Wright, G., Ayache, N., 2010b. Sermesant, M. Estimation of reaction, diffusion and restitution parameters for a 3D myocardial model using optical mapping and MRI, in: International workshop on Statistical Atlases and Computational Models of the Heart and Cardiac Electrophysiological Simulation Challenge (STACOM-CESC'10), LNCS 6364, pp. 270-280.
39. Rogers J.M., McCulloch A.D. A collocation-galerkin finite element model of cardiac action potential propagation. IEEE Trans. Biomed. Eng. 1994, 41:743-757.
40. Romero D., Sebastian R., Bijnens B., Zimmerman V., Boyle P., Vigmond E., Frangi A. Effects of the Purkinje system and cardiac geometry on biventricular pacing: a model study. Ann. Biomed. Eng. 2010, 38:1388-1398.
41. Roth B.J. Electrical conductivity values used with the bidomain model of cardiac tissue. IEEE Trans. Biomed. Eng. 1997, 44:326-328.
42. Sermesant M., Moireau P., Camara O., Sainte-Marie J., Andriantsimiavona R., Cimrman R., Hill D., Chapelle D., Razavi R. Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med. Image Anal. 2006, 10:642-656.
43. Sethian, J.A., 1996. A fast marching level set method for monotonically advancing fronts, volume 93, pp. 1591-1595.
44. Smith N., de Vecchi A., McCormick M., Nordsletten D., Camara O., Frangi A.F., Delingette H., Sermesant M., Relan J., Ayache N., Krueger M.W., Schulze W., Hose R., Valverde I., Beerbaum P., Staicu C., Siebes M., Spaan J., Hunter P., Weese J., Lehmann H., Chapelle D., Razavi R. euHeart: Personalized and integrated cardiac care using patient-specific cardiovascular modelling. J. Royal Soc. Interface Focus 2011, 1:349-364.
45. Strickberger S., Conti J., Daoud E., Havranek E., Mehra M., Peña I., Young J. Patient selection for cardiac resynchronization therapy. Circulation 2005, 111:2146-2150.
46. ten Tusscher K.H., Panfilov A.V. Alternans and spiral breakup in a human ventricular tissue model. Am. J. Physiol. Heart Circ. Physiol. 2006, 291:H1088-H1100.
47. Vigmond E.J., Hughes M., Plank G., Leon L.J. Computational tools for modeling electrical activity in cardiac tissue. J. Electrocardiol. 2003, 36(Suppl):69-74.
48. Wang V., Lam H., Ennis D., Cowan B., Young A., Nash M. Modelling passive diastolic mechanics with quantitative MRI of cardiac structure and function. Med. Image Anal. 2009, 13:773-784.
49. Wang L., Zhang H., Wong K., Liu H., Shi P. Physiological-model-constrained noninvasive reconstruction of volumetric myocardial transmembrane potentials. IEEE Trans. Biomed. Eng. 2010, 5:296-315.
50. Wang L., Wong K., Zhang H., Liu H., Shi P. Noninvasive computational imaging of cardiac electrophysiology for 3D infarct quantitation. IEEE Trans. Biomed. Eng. 2011, 58:1033-1043.
51. Wang, L., Wong, K., Zhang, H., Liu, H., Shi, P., 2010b. A statistical physiological-model-constrained framework for computational imaging of subject-specific volumetric cardiac electrophysiology using optical imaging and MRI data, in: International workshop on Statistical Atlases and Computational Models of the Heart and Cardiac Electrophysiological Simulation Challenge (STACOM-CESC'10), LNCS 6364, pp. 261-269.
52. Wong C.L., Zhang H.Y., Liu H.F., Shi P.C. Physiome-model-based state-space framework for cardiac deformation recovery. Acad. Radiol. 2007, 14:1341-1349.
53. Xi, J., Lamata, P., Shi, W., Niederer, S., Land, S., Rueckert, D., Duckett, S., Shetty, A., Rinaldi, A., Razavi, R., Smith, N., 2011. An automatic data assimilation framework for patient-specific myocardial mechanical parameter estimation, in: International Conference on Functional Imaging and Modeling of the Heart (FIMH'11), LNCS 6666, pp. 392-400.
54. Zimmerman, V., Sebastian, R., Bijnens, B., Frangi, A., 2009. Modeling the Purkinje conduction system with a non deterministic rule based iterative method, in: Computer in Cardiology, volume 36, pp. 461-464.