Embedding high-dimensional Bayesian optimization via generative modeling: Parameter personalization of cardiac electrophysiological models

Aliev, 1996, A simple two-variable model of cardiac excitation, Chaos Solitons Fractals, 7, 293, 10.1016/0960-0779(95)00089-5 Arevalo, 2016, Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models, Nat. Commun., 7, 10.1038/ncomms11437 Brochu, E., Cora, V. M., De Freitas, N., 2010. A tutorial on Bayesian optimization of expensive cost functions, with application to active user modeling and hierarchical reinforcement learning. arXiv:1012.2599. Bronstein, 2017, Geometric deep learning: going beyond Euclidean data, IEEE Signal Process. Mag., 34, 18, 10.1109/MSP.2017.2693418 Cerqueira, 2002, Standardized myocardial segmentation and nomenclature for tomographic imaging of the heart a statement for healthcare professionals from the cardiac imaging committee of the council on clinical cardiology of the American Heart Association, Circulation, 105, 539, 10.1161/hc0402.102975 Chinchapatnam, 2009, Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping, 2907 Clayton, 2011, Models of cardiac tissue electrophysiology: progress, challenges and open questions, Progress in Biophys. Mol. Biol., 104, 22, 10.1016/j.pbiomolbio.2010.05.008 Delingette, 2012, Personalization of cardiac motion and contractility from images using variational data assimilation, IEEE Trans. Biomed. Eng., 59, 20, 10.1109/TBME.2011.2160347 Dhamala, 2017, Spatially-adaptive multi-scale optimization for local parameter estimation in cardiac electrophysiology, IEEE Trans. Med. Imaging, 10.1109/TMI.2017.2697820 Dhamala, 2018, High-dimensional Bayesian optimization of personalized cardiac model parameters via an embedded generative model, 499 Dhamala, 2016, Spatially-adaptive multi-scale optimization for local parameter estimation: application in cardiac electrophysiological models, 282 Dhamala, 2017, Quantifying the uncertainty in model parameters using Gaussian process-based Markov Chain Monte Carlo: An application to cardiac electrophysiological models, 223 Fang, 2009, Tetrahedral mesh generation from volumetric binary and grayscale images, 1142 Giffard-Roisin, 2017, Noninvasive personalization of a cardiac electrophysiology model from body surface potential mapping, IEEE Trans. Biomed. Eng., 64, 2206, 10.1109/TBME.2016.2629849 Goodfellow, 2014, Generative adversarial nets, 2672 Hu, 2013, Effects of mechano-electric feedback on scroll wave stability in human ventricular fibrillation, PLoS One, 8, e60287, 10.1371/journal.pone.0060287 Kandasamy, 2015, High dimensional Bayesian optimisation and bandits via additive models, 295 Kingma Kingma Lê, 2016, MRI based Bayesian personalization of a tumor growth model, IEEE Trans. Med. Imaging, 35, 2329, 10.1109/TMI.2016.2561098 Li Maaten, 2008, Visualizing data using t-SNE, J. Mach. Learn. Res., 9, 2579 Makhzani Marchesseau, 2012, Cardiac mechanical parameter calibration based on the unscented transform, 41 Mitchell, 2003, A two-current model for the dynamics of cardiac membrane, Bull. Math. Biol., 65, 767, 10.1016/S0092-8240(03)00041-7 Moireau, 2013, Sequential identification of boundary support parameters in a fluid-structure vascular model using patient image data, Biomech. Modeling Mechanobiol., 12, 475, 10.1007/s10237-012-0418-3 Myronenko, 2010, Point set registration: Coherent point drift, IEEE Trans. Pattern Anal. Mach. Intell., 32, 2262, 10.1109/TPAMI.2010.46 Nash, 1998 Nielsen, 1991, Mathematical model of geometry and fibrous structure of the heart, Am. J. Physiol. Heart Circ. Physiol., 260, H1365, 10.1152/ajpheart.1991.260.4.H1365 Otsu, 1975, A threshold selection method from gray-level histograms, Automatica, 11, 23 Plonsey, 1969 Prakosa, 2018, Personalized virtual-heart technology for guiding the ablation of infarct-related ventricular tachycardia, Nat. Biomed. Eng., 2, 732, 10.1038/s41551-018-0282-2 Rasmussen, 2006, 1 Relan, 2011, Personalization of a cardiac electrophysiology model using optical mapping and MRI for prediction of changes with pacing, IEEE Transactions on Biomedical Engineering, 58, 3339, 10.1109/TBME.2011.2107513 Relan, 2009, Parameter estimation of a 3d cardiac electrophysiology model including the restitution curve using optical and MR data, 1716 Rezende Rogers, 1994, A collocation-galerkin finite element model of cardiac action potential propagation, IEEE Trans. Biomed. Eng., 41, 743, 10.1109/10.310090 Sapp, 2012, Inverse solution mapping of epicardial potentials: Quantitative comparison to epicardial contact mapping, Circ. Arrhythmia Electrophysiol., CIRCEP Sermesant, 2012, Patient-specific electromechanical models of the heart for the prediction of pacing acute effects in crt: a preliminary clinical validation, Med. Image Anal., 16, 201, 10.1016/j.media.2011.07.003 Shahriari, 2016, Taking the human out of the loop: A review of Bayesian optimization, Proc. IEEE, 104, 148, 10.1109/JPROC.2015.2494218 Siivola, 2018, Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations, 1 Taylor, 2009, Patient-specific modeling of cardiovascular mechanics, Annu. Rev. Biomed. Eng., 11, 109, 10.1146/annurev.bioeng.10.061807.160521 Tusscher, 2008, Modelling of the ventricular conduction system, Progr. Biophys. Mol. Biol., 96, 152, 10.1016/j.pbiomolbio.2007.07.026 Wang, 2018, Non-invasive epicardial and endocardial electrocardiographic imaging for scar-related ventricular tachycardia, Ep Europace, 20, f263, 10.1093/europace/euy082 Wang, 2010, Physiological-model-constrained noninvasive reconstruction of volumetric myocardial transmembrane potentials, IEEE Trans. Biomed. Eng., 57, 296, 10.1109/TBME.2009.2024531 Wong, 2012, Strain-based regional nonlinear cardiac material properties estimation from medical images, 617 Wong, 2015, Velocity-based cardiac contractility personalization from images using derivative-free optimization, J. Mech. Behav. Biomed. Mater., 43, 35, 10.1016/j.jmbbm.2014.12.002 Zahid, 2016, Feasibility of using patient-specific models and the “minimum cut” algorithm to predict optimal ablation targets for left atrial flutter., Heart Rhythm, 4, 1687, 10.1016/j.hrthm.2016.04.009 Zettinig, 2013, Fast data-driven calibration of a cardiac electrophysiology model from images and ECG, 1