Abstract
Post-ischemic Ventricular Tachycardia (VT) is sustained by a depolarization wave re-entry through channel-like structures within the post-ischemic scar. These structures are usually formed by partially viable tissue, called Border Zone (BZ). Understanding the anatomical and electrical properties of the BZ is crucial to guide ablation therapy to the right targets, reducing the likelihood of VT recurrence. Virtual Heart methods can provide ablation guidance non-invasively, but they have high computational complexity and have shown limited capability to accurately reproduce the specific mechanisms responsible for clinically observed VT. These outstanding challenges undermine the utility of Virtual Hearts for high precision ablation guidance in clinical practice. In this work, fast phenomenological models are developed to efficiently and accurately simulate the re-entrant dynamics of VT as observed in 12-lead ECG. Two porcine models of Myocardial Infarction (MI) are used to generate personalized bi-ventricular models from pre-operative LGE-MRI images. Myocardial conductivity and action potential duration are estimated using sinus rhythm ECG measurements. Multiple hypotheses for the BZ tissue properties are tested, and optimal values are identified. These allow the Virtual Heart model to produce VTs with good agreements with measurements in terms of ECG lead polarity and VT cycle length. Efficient GPU implementation of the cardiac electrophysiology model allows computation of sustained monomorphic VT in times compatible with the clinical workflow.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Andreu, D., et al.: A QRS axis-based algorithm to identify the origin of scar-related ventricular tachycardia in the 17-segment American Heart Association model. Heart Rhythm 15(10), 1491–1497 (2018)
Ashikaga, H., et al.: Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia. Heart Rhythm 10(8), 1109–1116 (2013)
Bayer, J.D., Blake, R.C., Plank, G., Trayanova, N.A.: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann. Biomed. Eng. 40, 2243–2254 (2012)
Campos, F.O., et al.: An automated near-real time computational method for induction and treatment of scar-related ventricular tachycardias. Med. Image Anal. 80, 102483 (2022)
Campos, F.O., et al.: Factors promoting conduction slowing as substrates for block and reentry in infarcted hearts. Biophys. J. 117(12), 2361–2374 (2019)
Corrado, C., Niederer, S.A.: A two-variable model robust to pacemaker behaviour for the dynamics of the cardiac action potential. Math. Biosci. 281, 46–54 (2016)
Costa, C.M., et al.: Determining anatomical and electrophysiological detail requirements for computational ventricular models of porcine myocardial infarction. Comput. Biol. Med. 141, 105061 (2022)
De Bakker, J., et al.: Reentry as a cause of ventricular tachycardia in patients with chronic ischemic heart disease: electrophysiologic and anatomic correlation. Circulation 77(3), 589–606 (1988)
Deng, D., Prakosa, A., Shade, J., Nikolov, P., Trayanova, N.A.: Sensitivity of ablation targets prediction to electrophysiological parameter variability in image-based computational models of ventricular tachycardia in post-infarction patients. Front. Physiol. 10, 628 (2019)
Durrer, D., Van Dam, R.T., Freud, G., Janse, M., Meijler, F., Arzbaecher, R.: Total excitation of the isolated human heart. Circulation 41(6), 899–912 (1970)
Estner, H.L., et al.: The critical isthmus sites of ischemic ventricular tachycardia are in zones of tissue heterogeneity, visualized by magnetic resonance imaging. Heart Rhythm 8(12), 1942–1949 (2011)
Gard, J.J., Bader, W., Enriquez-Sarano, M., Frye, R.L., Michelena, H.I.: Uncommon cause of ST elevation. Circulation 123(9), e259–e261 (2011)
Kong, W., Fakhari, N., Sharifov, O.F., Ideker, R.E., Smith, W.M., Fast, V.G.: Optical measurements of intramural action potentials in isolated porcine hearts using optrodes. Heart Rhythm 4(11), 1430–1436 (2007)
Lee, A.W., et al.: A rule-based method for predicting the electrical activation of the heart with cardiac resynchronization therapy from non-invasive clinical data. Med. Image Anal. 57, 197–213 (2019)
Lopez-Perez, A., Sebastian, R., Izquierdo, M., Ruiz, R., Bishop, M., Ferrero, J.M.: Personalized cardiac computational models: from clinical data to simulation of infarct-related ventricular tachycardia. Front. Physiol. 10, 580 (2019)
Mendonca Costa, C., Plank, G., Rinaldi, C.A., Niederer, S.A., Bishop, M.J.: Modeling the electrophysiological properties of the infarct border zone. Front. Physiol. 9, 356 (2018)
Mihalef, V., Mansi, T., Rapaka, S., Passerini, T.: Implementation of a patient-specific cardiac model. In: Artificial Intelligence for Computational Modeling of the Heart, pp. 43–94. Elsevier (2020)
Mihalef, V., Passerini, T., Mansi, T.: Multi-scale models of the heart for patient-specific simulations. In: Artificial Intelligence for Computational Modeling of the Heart, pp. 3–42. Elsevier (2020)
Mitchell, C.C., Schaeffer, D.G.: A two-current model for the dynamics of cardiac membrane. Bull. Math. Biol. 65(5), 767–793 (2003)
Prakosa, A., et al.: Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia. Nat. Biomed. Eng. 2(10), 732–740 (2018)
Rapaka, S., et al.: LBM-EP: Lattice-Boltzmann method for fast cardiac electrophysiology simulation from 3D images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7511, pp. 33–40. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33418-4_5
Santangeli, P., et al.: Comparative effectiveness of antiarrhythmic drugs and catheter ablation for the prevention of recurrent ventricular tachycardia in patients with implantable cardioverter-defibrillators: a systematic review and meta-analysis of randomized controlled trials. Heart Rhythm 13(7), 1552–1559 (2016)
Schmidt, A., et al.: Infarct tissue heterogeneity by magnetic resonance imaging identifies enhanced cardiac arrhythmia susceptibility in patients with left ventricular dysfunction. Circulation 115(15), 2006–2014 (2007)
Tranum-Jensen, J., Wilde, A., Vermeulen, J.T., Janse, M.J.: Morphology of electrophysiologically identified junctions between purkinje fibers and ventricular muscle in rabbit and pig hearts. Circ. Res. 69(2), 429–437 (1991)
Trayanova, N.A., Doshi, A.N., Prakosa, A.: How personalized heart modeling can help treatment of lethal arrhythmias: a focus on ventricular tachycardia ablation strategies in post-infarction patients. Wiley Interdiscip. Rev. Syst. Biol. Med. 12(3), e1477 (2020)
Zettinig, O., et al.: Data-driven estimation of cardiac electrical diffusivity from 12-lead ECG signals. Med. Image Anal. 18(8), 1361–1376 (2014)
Acknowledgements
The animal study was reviewed and approved by the Johns Hopkins University Animal Care and Use Committee (Baltimore, MD). Animal Welfare Assurance Number A3272-01.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Castañeda, E. et al. (2023). Virtual Heart Models Help Elucidate the Role of Border Zone in Sustained Monomorphic Ventricular Tachycardia. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-031-43990-2_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-43989-6
Online ISBN: 978-3-031-43990-2
eBook Packages: Computer ScienceComputer Science (R0)
