Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications

[EN] Electrocardiographic imaging (ECGI) reconstructs the electrical activity of the heart from a dense array of body-surface electrocardiograms and a patient-specific heart-torso geometry. Depending on how it is formulated, ECGI allows the reconstruction of the activation and recovery sequence of the heart, the origin of premature beats or tachycardia, the anchors/hotspots of re-entrant arrhythmias and other electrophysiological quantities of interest. Importantly, these quantities are directly and non-invasively reconstructed in a digitized model of the patient's three-dimensional heart, which has led to clinical interest in ECGI's ability to personalize diagnosis and guide therapy. Despite considerable development over the last decades, validation of ECGI is challenging. Firstly, results depend considerably on implementation choices, which are necessary to deal with ECGI's ill-posed character. Secondly, it is challenging to obtain (invasive) ground truth data of high quality. In this review, we discuss the current status of ECGI validation as well as the major challenges remaining for complete adoption of ECGI in clinical practice. Specifically, showing clinical benefit is essential for the adoption of ECGI. Such benefit may lie in patient outcome improvement, workflow improvement, or cost reduction. Future studies should focus on these aspects to achieve broad adoption of ECGI, but only after the technical challenges have been solved for that specific application/pathology. We propose 'best' practices for technical validation and highlight collaborative efforts recently organized in this field. Continued interaction between engineers, basic scientists, and physicians remains essential to find a hybrid between technical achievements, pathological mechanisms insights, and clinical benefit, to evolve this powerful technique toward a useful role in clinical practice.This study received financial support from the Hein Wellens Fonds, the Cardiovascular Research and Training Institute (CVRTI), the Nora Eccles Treadwell Foundation, the National Institute of General Medical Sciences of the National Institutes of Health (P41GM103545), the National Institutes of Health (NIH HL080093), the French government as part of the Investments of the Future program managed by the National Research Agency (ANR-10-IAHU-04), from the VEGA Grant Agency in Slovakia (2/0071/16), from the Slovak Research and Development Agency (APVV-14-0875), the Fondo Europeo de Desarrollo Regional (FEDER), the Instituto de Salud Carlos III (PI17/01106) and from Conselleria d'Educacio, Investigacio, Cultura i Esport de la Generalitat Valenciana (AICO/2018/267) and NIH grant (HL125998) and National Science Foundation (ACI-1350374).Cluitmans, M.; Brooks, D.; Macleod, RS.; Dossel, O.; Guillem Sánchez, MS.; Van Dam, P.; Svehlikova, J.... (2018). Validation and Opportunities of Electrocardiographic Imaging: From Technical chievements to Clinical Applications. Frontiers in Physiology. 9. https://doi.org/10.3389/fphys.2018.01305S9Andrews, C. M., Srinivasan, N. T., Rosmini, S., Bulluck, H., Orini, M., Jenkins, S., … Rudy, Y. (2017). Electrical and Structural Substrate of Arrhythmogenic Right Ventricular Cardiomyopathy Determined Using Noninvasive Electrocardiographic Imaging and Late Gadolinium Magnetic Resonance Imaging. Circulation: Arrhythmia and Electrophysiology, 10(7). doi:10.1161/circep.116.005105Aras, K., Good, W., Tate, J., Burton, B., Brooks, D., Coll-Font, J., … MacLeod, R. (2015). Experimental Data and Geometric Analysis Repository—EDGAR. Journal of Electrocardiology, 48(6), 975-981. doi:10.1016/j.jelectrocard.2015.08.008Austen, W., Edwards, J., Frye, R., Gensini, G., Gott, V., Griffith, L., … Roe, B. (1975). A reporting system on patients evaluated for coronary artery disease. Report of the Ad Hoc Committee for Grading of Coronary Artery Disease, Council on Cardiovascular Surgery, American Heart Association. Circulation, 51(4), 5-40. doi:10.1161/01.cir.51.4.5Bayley, R. H., & Berry, P. M. (1962). The electrical field produced by the eccentric current dipole in the nonhomogeneous conductor. American Heart Journal, 63(6), 808-820. doi:10.1016/0002-8703(62)90065-0Bear, L. R., Huntjens, P. R., Walton, R. D., Bernus, O., Coronel, R., & Dubois, R. (2018). Cardiac electrical dyssynchrony is accurately detected by noninvasive electrocardiographic imaging. Heart Rhythm, 15(7), 1058-1069. doi:10.1016/j.hrthm.2018.02.024Bear, L. R., LeGrice, I. J., Sands, G. B., Lever, N. A., Loiselle, D. S., Paterson, D. J., … Smaill, B. H. (2018). How Accurate Is Inverse Electrocardiographic Mapping? Circulation: Arrhythmia and Electrophysiology, 11(5). doi:10.1161/circep.117.006108Berger, T., Fischer, G., Pfeifer, B., Modre, R., Hanser, F., Trieb, T., … Hintringer, F. (2006). Single-Beat Noninvasive Imaging of Cardiac Electrophysiology of Ventricular Pre-Excitation. Journal of the American College of Cardiology, 48(10), 2045-2052. doi:10.1016/j.jacc.2006.08.019Berger, T., Pfeifer, B., Hanser, F. F., Hintringer, F., Fischer, G., Netzer, M., … Seger, M. (2011). Single-Beat Noninvasive Imaging of Ventricular Endocardial and Epicardial Activation in Patients Undergoing CRT. PLoS ONE, 6(1), e16255. doi:10.1371/journal.pone.0016255Dubois, R., Pashaei, A., Duchateau, J., & Vigmond, E. (2016). Evaluation of Combined Noninvasive Electrocardiographic Imaging and Phase Mapping approach for Atrial Fibrillation: A Simulation Study. 2016 Computing in Cardiology Conference (CinC). doi:10.22489/cinc.2016.037-540Duchateau, J., Potse, M., & Dubois, R. (2017). Spatially Coherent Activation Maps for Electrocardiographic Imaging. IEEE Transactions on Biomedical Engineering, 64(5), 1149-1156. doi:10.1109/tbme.2016.2593003Erem, B., Brooks, D. H., van Dam, P. M., Stinstra, J. G., & MacLeod, R. S. (2011). Spatiotemporal estimation of activation times of fractionated ECGs on complex heart surfaces. 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. doi:10.1109/iembs.2011.6091455Erem, B., van Dam, P. M., & Brooks, D. H. (2014). Identifying Model Inaccuracies and Solution Uncertainties in Noninvasive Activation-Based Imaging of Cardiac Excitation Using Convex Relaxation. IEEE Transactions on Medical Imaging, 33(4), 902-912. doi:10.1109/tmi.2014.2297952Erkapic, D., & Neumann, T. (2015). Ablation of Premature Ventricular Complexes Exclusively Guided by Three-Dimensional Noninvasive Mapping. Cardiac Electrophysiology Clinics, 7(1), 109-115. doi:10.1016/j.ccep.2014.11.010Everett, T. H., Lai-Chow Kok, Vaughn, R. H., Moorman, R., & Haines, D. E. (2001). Frequency domain algorithm for quantifying atrial fibrillation organization to increase defibrillation efficacy. IEEE Transactions on Biomedical Engineering, 48(9), 969-978. doi:10.1109/10.942586Faes, L., & Ravelli, F. (2007). A morphology-based approach to the evaluation of atrial fibrillation organization. IEEE Engineering in Medicine and Biology Magazine, 26(4), 59-67. doi:10.1109/memb.2007.384097Fitzpatrick, A. P., Gonzales, R. P., Lesh, M. D., odin, G. W., Lee, R. J., & Scheinman, M. M. (1994). New algorithm for the localization of accessory atrioventricular connections using a baseline electrocardiogram. Journal of the American College of Cardiology, 23(1), 107-116. doi:10.1016/0735-1097(94)90508-8Geselowitz, D. B. (1989). On the theory of the electrocardiogram. Proceedings of the IEEE, 77(6), 857-876. doi:10.1109/5.29327Geselowitz, D. B. (1992). Description of cardiac sources in anisotropic cardiac muscle. Journal of Electrocardiology, 25, 65-67. doi:10.1016/0022-0736(92)90063-6Ghanem, R. N., Jia, P., Ramanathan, C., Ryu, K., Markowitz, A., & Rudy, Y. (2005). Noninvasive Electrocardiographic Imaging (ECGI): Comparison to intraoperative mapping in patients. Heart Rhythm, 2(4), 339-354. doi:10.1016/j.hrthm.2004.12.022Ghosh, S., Rhee, E. K., Avari, J. N., Woodard, P. K., & Rudy, Y. (2008). Cardiac Memory in Patients With Wolff-Parkinson-White Syndrome. Circulation, 118(9), 907-915. doi:10.1161/circulationaha.108.781658Ghosh, S., Silva, J. N. A., Canham, R. M., Bowman, T. M., Zhang, J., Rhee, E. K., … Rudy, Y. (2011). Electrophysiologic substrate and intraventricular left ventricular dyssynchrony in nonischemic heart failure patients undergoing cardiac resynchronization therapy. Heart Rhythm, 8(5), 692-699. doi:10.1016/j.hrthm.2011.01.017Grace, A., Verma, A., & Willems, S. (2017). Dipole Density Mapping of Atrial Fibrillation. European Heart Journal, 38(1), 5-9. doi:10.1093/eurheartj/ehw585Dorset, D. L. (1996). Electron crystallography. Acta Crystallographica Section B Structural Science, 52(5), 753-769. doi:10.1107/s0108768196005599Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421HAISSAGUERRE, M., HOCINI, M., SHAH, A. J., DERVAL, N., SACHER, F., JAIS, P., & DUBOIS, R. (2013). Noninvasive Panoramic Mapping of Human Atrial Fibrillation Mechanisms: A Feasibility Report. Journal of Cardiovascular Electrophysiology, 24(6), 711-717. doi:10.1111/jce.12075Han, C., Pogwizd, S. M., Killingsworth, C. R., & He, B. (2011). Noninvasive imaging of three-dimensional cardiac activation sequence during pacing and ventricular tachycardia. Heart Rhythm, 8(8), 1266-1272. doi:10.1016/j.hrthm.2011.03.014Bin He, Guanglin Li, & Xin Zhang. (2003). Noninvasive imaging of cardiac transmembrane potentials within three-dimensional myocardium by means of a realistic geometry anisotropic heart model. IEEE Transactions on Biomedical Engineering, 50(10), 1190-1202. doi:10.1109/tbme.2003.817637Bin He, & Dongsheng Wu. (2001). Imaging and visualization of 3-D cardiac electric activity. IEEE Transactions on Information Technology in Biomedicine, 5(3), 181-186. doi:10.1109/4233.945288Horáček, B. M., Sapp, J. L., Penney, C. J., Warren, J. W., & Wang, J. J. (2011). Comparison of epicardial potential maps derived from the 12-lead electrocardiograms with scintigraphic images during controlled myocardial ischemia. Journal of Electrocardiology, 44(6), 707-712. doi:10.1016/j.jelectrocard.2011.08.009Horáček, B. M., Wang, L., Dawoud, F., Xu, J., & Sapp, J. L. (2015). Noninvasive electrocardiographic imaging of chronic myocardial infarct scar. Journal of Electrocardiology, 48(6), 952-958. doi:10.1016/j.jelectrocard.2015.08.035Jamil-Copley, S., Vergara, P., Carbucicchio, C., Linton, N., Koa-Wing, M., Luther, V., … Kanagaratnam, P. (2015). Application of Ripple Mapping to Visualize Slow Conduction Channels Within the Infarct-Related Left Ventricular Scar. Circulation: Arrhythmia and Electrophysiology, 8(1), 76-86. doi:10.1161/circep.114.001827Janssen, A. M., Potyagaylo, D., Dössel, O., & Oostendorp, T. F. (2017). Assessment of the equivalent dipole layer source model in the reconstruction of cardiac activation times on the basis of BSPMs produced by an anisotropic model of the heart. Medical & Biological Engineering & Computing, 56(6), 1013-1025. doi:10.1007/s11517-017-1715-xKnecht, S., Sohal, M., Deisenhofer, I., Albenque, J.-P., Arentz, T., Neumann, T., … Rostock, T. (2017). Multicentre evaluation of non-invasive biatrial mapping for persistent atrial fibrillation ablation: the AFACART study. EP Europace, 19(8), 1302-1309. doi:10.1093/europace/euw168Kuck, K.-H., Schaumann, A., Eckardt, L., Willems, S., Ventura, R., Delacrétaz, E., … Hansen, P. S. (2010). Catheter ablation of stable ventricular tachycardia before defibrillator implantation in patients with coronary heart disease (VTACH): a multicentre randomised controlled trial. The Lancet, 375(9708), 31-40. doi:10.1016/s0140-6736(09)61755-4Identification of Rotors during Human Atrial Fibrillation Using Contact Mapping and Phase Singularity Detection: Technical Considerations. (2017). IEEE Transactions on Biomedical Engineering, 64(2), 310-318. doi:10.1109/tbme.2016.2554660Leong, K. M. W., Ng, F. S., Yao, C., Roney, C., Taraborrelli, P., Linton, N. W. F., … Varnava, A. M. (2017). ST-Elevation Magnitude Correlates With Right Ventricular Outflow Tract Conduction Delay in Type I Brugada ECG. Circulation: Arrhythmia and Electrophysiology, 10(10). doi:10.1161/circep.117.005107Chenguang Liu, Eggen, M. D., Swingen, C. M., Iaizzo, P. A., & Bin He. (2012). Noninvasive Mapping of Transmural Potentials During Activation in Swine Hearts From Body Surface Electrocardiograms. IEEE Transactions on Medical Imaging, 31(9), 1777-1785. doi:10.1109/tmi.2012.2202914MacLeod, R. S., Ni, Q., Punske, B., Ershler, P. R., Yilmaz, B., & Taccardi, B. (2000). Effects of heart position on the body-surface electrocardiogram. Journal of Electrocardiology, 33, 229-237. doi:10.1054/jelc.2000.20357Metzner, A., Wissner, E., Tsyganov, A., Kalinin, V., Schlüter, M., Lemes, C., … Kuck, K.-H. (2017). Noninvasive phase mapping of persistent atrial fibrillation in humans: Comparison with invasive catheter mapping. Annals of Noninvasive Electrocardiology, 23(4), e12527. doi:10.1111/anec.12527Modre, R., Tilg, B., Fischer, G., Hanser, F., Messnarz, B., Seger, M., … Roithinger, F. X. (2003). Atrial Noninvasive Activation Mapping of Paced Rhythm Data. Journal of Cardiovascular Electrophysiology, 14(7), 712-719. doi:10.1046/j.1540-8167.2003.02558.xNarayan, S. M., Krummen, D. E., Shivkumar, K., Clopton, P., Rappel, W.-J., & Miller, J. M. (2012). Treatment of Atrial Fibrillation by the Ablation of Localized Sources. Journal of the American College of Cardiology, 60(7), 628-636. doi:10.1016/j.jacc.2012.05.022NG, J., KADISH, A. H., & GOLDBERGER, J. J. (2007). Technical Considerations for Dominant Frequency Analysis. Journal of Cardiovascular Electrophysiology, 18(7), 757-764. doi:10.1111/j.1540-8167.2007.00810.xOosterhoff, P., Meijborg, V. M. F., van Dam, P. M., van Dessel, P. F. H. M., Belterman, C. N. W., Streekstra, G. J., … Oostendorp, T. F. (2016). Experimental Validation of Noninvasive Epicardial and Endocardial Activation Imaging. Circulation: Arrhythmia and Electrophysiology, 9(8). doi:10.1161/circep.116.004104Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1997). Noninvasive Electrocardiographic Imaging. Circulation, 96(3), 1012-1024. doi:10.1161/01.cir.96.3.1012Oster, H. S., Taccardi, B., Lux, R. L., Ershler, P. R., & Rudy, Y. (1998). Electrocardiographic Imaging. Circulation, 97(15), 1496-1507. doi:10.1161/01.cir.97.15.1496PEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Ploux, S., Lumens, J., Whinnett, Z., Montaudon, M., Strom, M., Ramanathan, C., … Bordachar, P. (2013). Noninvasive Electrocardiographic Mapping to Improve Patient Selection for Cardiac Resynchronization Therapy. Journal of the American College of Cardiology, 61(24), 2435-2443. doi:10.1016/j.jacc.2013.01.093Potyagaylo, D., Segel, M., Schulze, W. H. W., & Dössel, O. (2013). Noninvasive Localization of Ectopic Foci: A New Optimization Approach for Simultaneous Reconstruction of Transmembrane Voltages and Epicardial Potentials. Lecture Notes in Computer Science, 166-173. doi:10.1007/978-3-642-38899-6_20Punshchykova, O., Švehlíková, J., Tyšler, M., Grünes, R., Sedova, K., Osmančík, P., … Kneppo, P. (2016). Influence of Torso Model Complexity on the Noninvasive Localization of Ectopic Ventricular Activity. Measurement Science Review, 16(2), 96-102. doi:10.1515/msr-2016-0013RAMANATHAN, C., & RUDY, Y. (2001). Electrocardiographic Imaging: II. Effect of Torso Inhomogeneities on Noninvasive Reconstruction of Epicardial Potentials, Electrograms, and Isochrones. Journal of Cardiovascular Electrophysiology, 12(2), 241-252. doi:10.1046/j.1540-8167.2001.00241.xReddy, V. Y., Reynolds, M. R., Neuzil, P., Richardson, A. W., Taborsky, M., Jongnarangsin, K., … Josephson, M. E. (2007). Prophylactic Catheter Ablation for the Prevention of Defibrillator Therapy. New England Journal of Medicine, 357(26), 2657-2665. doi:10.1056/nejmoa065457Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008ROTEN, L., PEDERSEN, M., PASCALE, P., SHAH, A., ELIAUTOU, S., SCHERR, D., … HAÏSSAGUERRE, M. (2012). Noninvasive Electrocardiographic Mapping for Prediction of Tachycardia Mechanism and Origin of Atrial Tachycardia Following Bilateral Pulmonary Transplantation. Journal of Cardiovascular Electrophysiology, 23(5), 553-555. doi:10.1111/j.1540-8167.2011.02250.xRudy, Y. (2013). Noninvasive Electrocardiographic Imaging of Arrhythmogenic Substrates in Humans. Circulation Research, 112(5), 863-874. doi:10.1161/circresaha.112.279315Ghosh, S., Avari, J. N., Rhee, E. K., Woodard, P. K., & Rudy, Y. (2008). Noninvasive electrocardiographic imaging (ECGI) of epicardial activation before and after catheter ablation of the accessory pathway in a patient with Ebstein anomaly. Heart Rhythm, 5(6), 857-860. doi:10.1016/j.hrthm.2008.03.011Rudy, Y., Plonsey, R., & Liebman, J. (1979). The effects of variations in conductivity and geometrical parameters on the electrocardiogram, using an eccentric spheres model. Circulation Research, 44(1), 104-111. doi:10.1161/01.res.44.1.104SALINET, J. L., TUAN, J. H., SANDILANDS, A. J., STAFFORD, P. J., SCHLINDWEIN, F. S., & NG, G. A. (2013). Distinctive Patterns of Dominant Frequency Trajectory Behavior in Drug-Refractory Persistent Atrial Fibrillation: Preliminary Characterization of Spatiotemporal Instability. Journal of Cardiovascular Electrophysiology, 25(4), 371-379. doi:10.1111/jce.12331Dalu, Y. (1978). Relating the multipole moments of the heart to activated parts of the epicardium and endocardium. Annals of Biomedical Engineering, 6(4), 492-505. doi:10.1007/bf02584552Sánchez, C., Bueno-Orovio, A., Pueyo, E., & Rodríguez, B. (2017). Atrial Fibrillation Dynamics and Ionic Block Effects in Six Heterogeneous Human 3D Virtual Atria with Distinct Repolarization Dynamics. Frontiers in Bioengineering and Biotechnology, 5. doi:10.3389/fbioe.2017.00029Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.517011Sapp, J. L., Bar-Tal, M., Howes, A. J., Toma, J. E., El-Damaty, A., Warren, J. W., … Horáček, B. M. (2017). Real-Time Localization of Ventricular Tachycardia Origin From the 12-Lead Electrocardiogram. JACC: Clinical Electrophysiology, 3(7), 687-699. doi:10.1016/j.jacep.2017.02.024Sapp, J. L., Dawoud, F., Clements, J. C., & Horáček, B. M. (2012). Inverse Solution Mapping of Epicardial Potentials. Circulation: Arrhythmia and Electrophysiology, 5(5), 1001-1009. doi:10.1161/circep.111.970160Sapp, J. L., Wells, G. A., Parkash, R., Stevenson, W. G., Blier, L., Sarrazin, J.-F., … Tang, A. S. L. (2016). Ventricular Tachycardia Ablation versus Escalation of Antiarrhythmic Drugs. New England Journal of Medicine, 375(2), 111-121. doi:10.1056/nejmoa1513614Schulze, W. H. W., Chen, Z., Relan, J., Potyagaylo, D., Krueger, M. W., Karim, R., … Dössel, O. (2016). ECG imaging of ventricular tachycardia: evaluation against simultaneous non-contact mapping and CMR-derived grey zone. Medical & Biological Engineering & Computing, 55(6), 979-990. doi:10.1007/s11517-016-1566-xShah, D. C., Jaïs, P., Haïssaguerre, M., Chouairi, S., Takahashi, A., Hocini, M., … Clémenty, J. (1997). Three-dimensional Mapping of the Common Atrial Flutter Circuit in the Right Atrium. Circulation, 96(11), 3904-3912. doi:10.1161/01.cir.96.11.3904Shome, S., & Macleod, R. (s. f.). Simultaneous High-Resolution Electrical Imaging of Endocardial, Epicardial and Torso-Tank Surfaces Under Varying Cardiac Metabolic Load and Coronary Flow. Lecture Notes in Computer Science, 320-329. doi:10.1007/978-3-540-72907-5_33SIMMS, H. D., & GESELOWITZ, D. B. (1995). Computation of Heart Surface Potentials Using the Surface Source Model. Journal of Cardiovascular Electrophysiology, 6(7), 522-531. doi:10.1111/j.1540-8167.1995.tb00425.xSvehlikova, J., Teplan, M., & Tysler, M. (2018). Geometrical constraint of sources in noninvasive localization of premature ventricular contractions. Journal of Electrocardiology, 51(3), 370-377. doi:10.1016/j.jelectrocard.2018.02.013Tsyganov, A., Wissner, E., Metzner, A., Mironovich, S., Chaykovskaya, M., Kalinin, V., … Kuck, K.-H. (2018). Mapping of ventricular arrhythmias using a novel noninvasive epicardial and endocardial electrophysiology system. Journal of Electrocardiology, 51(1), 92-98. doi:10.1016/j.jelectrocard.2017.07.018Umapathy, K., Nair, K., Masse, S., Krishnan, S., Rogers, J., Nash, M. P., & Nanthakumar, K. (2010). Phase Mapping of Cardiac Fibrillation. Circulation: Arrhythmia and Electrophysiology, 3(1), 105-114. doi:10.1161/circep.110.853804Van Dam, P. M., Oostendorp, T. F., Linnenbank, A. C., & van Oosterom, A. (2009). Non-Invasive Imaging of Cardiac Activation and Recovery. Annals of Biomedical Engineering, 37(9), 1739-1756. doi:10.1007/s10439-009-9747-5Van Oosterom, A. (2001). Genesis of the T wave as based on an equivalent surface source model. Journal of Electrocardiology, 34(4), 217-227. doi:10.1054/jelc.2001.28896Van Oosterom, A. (2002). Solidifying the solid angle. Journal of Electrocardiology, 35(4), 181-192. doi:10.1054/jelc.2002.37176Van Oosterom, A. (2004). ECGSIM: an interactive tool for stu

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 162, January 1977

This bibliography lists 189 reports, articles, and other documents introduced into the NASA scientific and technical information system in December 1976

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

Atrial location optimization by electrical measures for Electrocardiographic Imaging

[EN] Background: The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution. Methods: In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for +/- 2.5 cm and +/- 30 degrees on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data. Results: The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 +/- 0.5 cm in position and 16.0 +/- 10.7 degrees in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 +/- 1.1 Hz to 0.5 +/- 1.0 Hz and in the phase singularity position from 7.2 +/- 4.0 cm to 1.6 +/- 1.7 cm (p < 0.01). Conclusions: This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.This work was supported in part by: Generalitat Valenciana Grants [APOSTD/2017] and projects [GVA/2018/103]; Nvidia Corporation with GPU QUADRO P6000 donation.Gisbert Soler, V.; Jiménez-Serrano, S.; Roses-Albert, E.; Rodrigo Bort, M. (2020). Atrial location optimization by electrical measures for Electrocardiographic Imaging. Computers in Biology and Medicine. 127:1-8. https://doi.org/10.1016/j.compbiomed.2020.104031S18127Cuculich, P. S., Zhang, J., Wang, Y., Desouza, K. A., Vijayakumar, R., Woodard, P. K., & Rudy, Y. (2011). The Electrophysiological Cardiac Ventricular Substrate in Patients After Myocardial Infarction. Journal of the American College of Cardiology, 58(18), 1893-1902. doi:10.1016/j.jacc.2011.07.029Revishvili, A. S., Wissner, E., Lebedev, D. S., Lemes, C., Deiss, S., Metzner, A., … Kuck, K.-H. (2015). Validation of the mapping accuracy of a novel non-invasive epicardial and endocardial electrophysiology system. Europace, 17(8), 1282-1288. doi:10.1093/europace/euu339Haissaguerre, M., Hocini, M., Denis, A., Shah, A. J., Komatsu, Y., Yamashita, S., … Dubois, R. (2014). Driver Domains in Persistent Atrial Fibrillation. Circulation, 130(7), 530-538. doi:10.1161/circulationaha.113.005421PEDRÓN-TORRECILLA, J., RODRIGO, M., CLIMENT, A. M., LIBEROS, A., PÉREZ-DAVID, E., BERMEJO, J., … GUILLEM, M. S. (2016). Noninvasive Estimation of Epicardial Dominant High-Frequency Regions During Atrial Fibrillation. Journal of Cardiovascular Electrophysiology, 27(4), 435-442. doi:10.1111/jce.12931Cuculich, P. S., Wang, Y., Lindsay, B. D., Faddis, M. N., Schuessler, R. B., Damiano, R. J., … Rudy, Y. (2010). Noninvasive Characterization of Epicardial Activation in Humans With Diverse Atrial Fibrillation Patterns. Circulation, 122(14), 1364-1372. doi:10.1161/circulationaha.110.945709Wang, Y., Schuessler, R. B., Damiano, R. J., Woodard, P. K., & Rudy, Y. (2007). Noninvasive electrocardiographic imaging (ECGI) of scar-related atypical atrial flutter. Heart Rhythm, 4(12), 1565-1567. doi:10.1016/j.hrthm.2007.08.019Milan Horáček, B., & Clements, J. C. (1997). The inverse problem of electrocardiography: A solution in terms of single- and double-layer sources on the epicardial surface. Mathematical Biosciences, 144(2), 119-154. doi:10.1016/s0025-5564(97)00024-2Rodrigo, M., Climent, A. M., Liberos, A., Hernandez-Romero, I., Arenal, A., Bermejo, J., … Guillem, M. S. (2018). Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality. IEEE Transactions on Medical Imaging, 37(3), 733-740. doi:10.1109/tmi.2017.2707413Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. Medical & Biological Engineering & Computing, 50(8), 773-799. doi:10.1007/s11517-012-0924-6Koivumäki, J. T., Seemann, G., Maleckar, M. M., & Tavi, P. (2014). In Silico Screening of the Key Cellular Remodeling Targets in Chronic Atrial Fibrillation. PLoS Computational Biology, 10(5), e1003620. doi:10.1371/journal.pcbi.1003620Garcia-Molla, V. M., Liberos, A., Vidal, A., Guillem, M. S., Millet, J., Gonzalez, A., … Climent, A. M. (2014). Adaptive step ODE algorithms for the 3D simulation of electric heart activity with graphics processing units. Computers in Biology and Medicine, 44, 15-26. doi:10.1016/j.compbiomed.2013.10.023Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Highest dominant frequency and rotor positions are robust markers of driver location during noninvasive mapping of atrial fibrillation: A computational study. Heart Rhythm, 14(8), 1224-1233. doi:10.1016/j.hrthm.2017.04.017Dolan, E. D., Lewis, R. M., & Torczon, V. (2003). On the Local Convergence of Pattern Search. SIAM Journal on Optimization, 14(2), 567-583. doi:10.1137/s1052623400374495Rodrigo, M., Guillem, M. S., Climent, A. M., Pedrón-Torrecilla, J., Liberos, A., Millet, J., … Berenfeld, O. (2014). Body surface localization of left and right atrial high-frequency rotors in atrial fibrillation patients: A clinical-computational study. Heart Rhythm, 11(9), 1584-1591. doi:10.1016/j.hrthm.2014.05.013Sanders, P., Berenfeld, O., Hocini, M., Jaïs, P., Vaidyanathan, R., Hsu, L.-F., … Haïssaguerre, M. (2005). Spectral Analysis Identifies Sites of High-Frequency Activity Maintaining Atrial Fibrillation in Humans. Circulation, 112(6), 789-797. doi:10.1161/circulationaha.104.517011Atienza, F., Almendral, J., Ormaetxe, J. M., Moya, Á., Martínez-Alday, J. D., Hernández-Madrid, A., … Jalife, J. (2014). Comparison of Radiofrequency Catheter Ablation of Drivers and Circumferential Pulmonary Vein Isolation in Atrial Fibrillation. Journal of the American College of Cardiology, 64(23), 2455-2467. doi:10.1016/j.jacc.2014.09.053Rodrigo, M., Climent, A. M., Liberos, A., Fernández-Avilés, F., Berenfeld, O., Atienza, F., & Guillem, M. S. (2017). Technical Considerations on Phase Mapping for Identification of Atrial Reentrant Activity in Direct- and Inverse-Computed Electrograms. Circulation: Arrhythmia and Electrophysiology, 10(9). doi:10.1161/circep.117.005008Miller, J. M., Kalra, V., Das, M. K., Jain, R., Garlie, J. B., Brewster, J. A., & Dandamudi, G. (2017). Clinical Benefit of Ablating Localized Sources for Human Atrial Fibrillation. Journal of the American College of Cardiology, 69(10), 1247-1256. doi:10.1016/j.jacc.2016.11.079Perez-Alday, E. A., Thomas, J. A., Kabir, M., Sedaghat, G., Rogovoy, N., van Dam, E., … Tereshchenko, L. G. (2018). Torso geometry reconstruction and body surface electrode localization using three-dimensional photography. Journal of Electrocardiology, 51(1), 60-67. doi:10.1016/j.jelectrocard.2017.08.035Schulze, W. H. W., Mackens, P., Potyagaylo, D., Rhode, K., Tülümen, E., Schimpf, R., … Dössel, O. (2014). Automatic camera-based identification and 3-D reconstruction of electrode positions in electrocardiographic imaging. Biomedical Engineering / Biomedizinische Technik, 59(6). doi:10.1515/bmt-2014-0018Ghanem, R. N., Ramanathan, C., Ping Jia, & Rudy, Y. (2003). Heart-surface reconstruction and ecg electrodes localization using fluoroscopy, epipolar geometry and stereovision: application to noninvasive imaging of cardiac electrical activity. IEEE Transactions on Medical Imaging, 22(10), 1307-1318. doi:10.1109/tmi.2003.818263Lee, J., Thornhill, R. E., Nery, P., Robert deKemp, Peña, E., Birnie, D., … Ukwatta, E. (2019). Left atrial imaging and registration of fibrosis with conduction voltages using LGE-MRI and electroanatomical mapping. Computers in Biology and Medicine, 111, 103341. doi:10.1016/j.compbiomed.2019.103341Weiss, E., Wijesooriya, K., Dill, S. V., & Keall, P. J. (2007). Tumor and normal tissue motion in the thorax during respiration: Analysis of volumetric and positional variations using 4D CT. International Journal of Radiation Oncology*Biology*Physics, 67(1), 296-307. doi:10.1016/j.ijrobp.2006.09.009Wikström, K., Isacsson, U., Nilsson, K., & Ahnesjö, A. (2018). Reproducibility of heart and thoracic wall position in repeated deep inspiration breath holds for radiotherapy of left-sided breast cancer patients. Acta Oncologica, 57(10), 1318-1324. doi:10.1080/0284186x.2018.1490027Messinger-Rapport, B. J., & Rudy, Y. (1986). The Inverse Problem in Electrocardiography: A Model Study of the Effects of Geometry and Conductivity Parameters on the Reconstruction of Epicardial Potentials. IEEE Transactions on Biomedical Engineering, BME-33(7), 667-676. doi:10.1109/tbme.1986.325756Messinger-Rapport, B. J., & Rudy, Y. (1990). Noninvasive recovery of epicardial potentials in a realistic heart-torso geometry. Normal sinus rhythm. Circulation Research, 66(4), 1023-1039. doi:10.1161/01.res.66.4.1023Coll-Font, J., & Brooks, D. H. (2018). Tracking the Position of the Heart From Body Surface Potential Maps and Electrograms. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01727Van der Waal, J., Meijborg, V., Schuler, S., Coronel, R., & Oostendorp, T. (2020). In silico validation of electrocardiographic imaging to reconstruct the endocardial and epicardial repolarization pattern using the equivalent dipole layer source model. Medical & Biological Engineering & Computing, 58(8), 1739-1749. doi:10.1007/s11517-020-02203-yChamorro-Servent, J., Dubois, R., & Coudière, Y. (2019). Considering New Regularization Parameter-Choice Techniques for the Tikhonov Method to Improve the Accuracy of Electrocardiographic Imaging. Frontiers in Physiology, 10. doi:10.3389/fphys.2019.0027

ECG Imaging of Ventricular Activity in Clinical Applications

ECG imaging was performed in humans to reconstruct ventricular activation patterns and localize their excitation origins. The precision of the non-invasive reconstructions was evaluated against invasive measurements and found to be in line with the state-of-the-art literature. Statistics were produced for various excitation origins and reveal the beat-to-beat robustness of the imaging method