Optimal contrast-enhanced MRI image thresholding for accurate prediction of ventricular tachycardia using ex-vivo high resolution models

Patient specific models created from contrast-enhanced (i.e. late-gadolinium, LGE) MRI images can be used for prediction of reentry location and clinical ablation planning. However, there is still a need for direct and systematic comparison between characteristics of ventricular tachycardia (VT) morphologies predicted in computational models and those acquired in clinical or experimental protocols. In this study, we aimed to: 1) assess the differences in VT morphologies predicted by modeling and recorded in experiments in terms of patterns and location of reentries, earliest and latest activation sites, and cycle lengths; and 2) define the optimal range of infarct tissue threshold values which provide best match between simulation and experimental results. To achieve these goals, we utilized LGE-MRI images from 4 swine hearts with inducible monomorphic VT. The images were segmented to identify non-infarcted myocardium, semi viable gray zone (GZ), and core scar based on pixel intensity. Several models were reconstructed from each LGE-MRI scan, with voxels of intensity between that of non-infarcted myocardium and 20-50% of the maximum intensity (in 10% increments) in the infarct region classified as GZ. VT induction was simulated in each model. Our simulation results showed that using GZ intensity thresholds of 20% or 30% resulted in the best match of simulated propagation patterns and reentry locations with those from the experiment. Overall, we matched 70% (7/10) morphologies for all the hearts. Our simulation shows that MRI-based computational models of hearts with myocardial infarction can accurately reproduce the majority of experimentally recorded post-infarction VTs.

[1]  H. Halperin,et al.  Role of 3-Dimensional Architecture of Scar and Surviving Tissue in Ventricular Tachycardia: Insights From High-Resolution Ex Vivo Porcine Models , 2018, Circulation. Arrhythmia and electrophysiology.

[2]  Henry Halperin,et al.  Accuracy of prediction of infarct-related arrhythmic circuits from image-based models reconstructed from low and high resolution MRI , 2015, Front. Physiol..

[3]  Nicholas Ayache,et al.  Correspondence Between Simple 3-D MRI-Based Computer Models and In-Vivo EP Measurements in Swine With Chronic Infarctions , 2011, IEEE Transactions on Biomedical Engineering.

[4]  Oscar Camara,et al.  Three-Dimensional Architecture of Scar and Conducting Channels Based on High Resolution ce-CMR: Insights for Ventricular Tachycardia Ablation , 2013, Circulation. Arrhythmia and electrophysiology.

[5]  Gernot Plank,et al.  Automatically Generated, Anatomically Accurate Meshes for Cardiac Electrophysiology Problems , 2009, IEEE Transactions on Biomedical Engineering.

[6]  G. Plank,et al.  A Novel Rule-Based Algorithm for Assigning Myocardial Fiber Orientation to Computational Heart Models , 2012, Annals of Biomedical Engineering.

[7]  James C Carr,et al.  Virtual electrophysiological study in a 3-dimensional cardiac magnetic resonance imaging model of porcine myocardial infarction. , 2012, Journal of the American College of Cardiology.

[8]  Peter Kellman,et al.  Late Gadolinium-Enhancement Cardiac Magnetic Resonance Identifies Postinfarction Myocardial Fibrosis and the Border Zone at the Near Cellular Level in Ex Vivo Rat Heart , 2010, Circulation. Cardiovascular imaging.

[9]  Henry R. Halperin,et al.  Magnetic Resonance–Based Anatomical Analysis of Scar-Related Ventricular Tachycardia: Implications for Catheter Ablation , 2007, Circulation research.

[10]  J. Brugada,et al.  Integration of 3D Electroanatomic Maps and Magnetic Resonance Scar Characterization Into the Navigation System to Guide Ventricular Tachycardia Ablation , 2011, Circulation. Arrhythmia and electrophysiology.

[11]  Mercedes Ortiz,et al.  Tachycardia-Related Channel in the Scar Tissue in Patients With Sustained Monomorphic Ventricular Tachycardias: Influence of the Voltage Scar Definition , 2004, Circulation.

[12]  N. Trayanova,et al.  Patient-derived models link re-entrant driver localization in atrial fibrillation to fibrosis spatial pattern. , 2016, Cardiovascular research.

[13]  Hiroshi Ashikaga,et al.  The critical isthmus sites of ischemic ventricular tachycardia are in zones of tissue heterogeneity, visualized by magnetic resonance imaging. , 2011, Heart rhythm.

[14]  Vijay Devabhaktuni,et al.  Corrigendum to “Effects of Fibrosis Morphology on Reentrant Ventricular Tachycardia Inducibility and Simulation Fidelity in Patient-Derived Models” , 2014, Clinical Medicine Insights. Cardiology.

[15]  Edward Vigmond,et al.  Towards predictive modelling of the electrophysiology of the heart , 2009, Experimental physiology.

[16]  Bruce H Smaill,et al.  High-Resolution 3-Dimensional Reconstruction of the Infarct Border Zone: Impact of Structural Remodeling on Electrical Activation , 2012, Circulation research.

[17]  Gernot Plank,et al.  Tachycardia in Post-Infarction Hearts: Insights from 3D Image-Based Ventricular Models , 2013, PloS one.

[18]  Natalia A Trayanova,et al.  How computer simulations of the human heart can improve anti‐arrhythmia therapy , 2016, The Journal of physiology.

[19]  Alejandro F. Frangi,et al.  Evaluation of state-of-the-art segmentation algorithms for left ventricle infarct from late Gadolinium enhancement MR images , 2016, Medical Image Anal..

[20]  Katherine C. Wu,et al.  Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models , 2016, Nature Communications.

[21]  P. Ursell,et al.  Structural and Electrophysiological Changes in the Epicardial Border Zone of Canine Myocardial Infarcts during Infarct Healing , 1985, Circulation research.

[22]  E. Vigmond,et al.  The Role of Purkinje-Myocardial Coupling during Ventricular Arrhythmia: A Modeling Study , 2014, PloS one.

[23]  Hiroshi Ashikaga,et al.  Feasibility of image-based simulation to estimate ablation target in human ventricular arrhythmia. , 2013, Heart rhythm.

[24]  Vivek Muthurangu,et al.  Evaluation of techniques for the quantification of myocardial scar of differing etiology using cardiac magnetic resonance. , 2011, JACC. Cardiovascular imaging.

[25]  Capelle,et al.  Slow conduction in the infarcted human heart. 'Zigzag' course of activation. , 1993, Circulation.

[26]  Gernot Plank,et al.  Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function , 2009, American journal of physiology. Heart and circulatory physiology.

[27]  Natalia A Trayanova,et al.  Sensitivity of reentrant driver localization to electrophysiological parameter variability in image-based computational models of persistent atrial fibrillation sustained by a fibrotic substrate. , 2017, Chaos.

[28]  Mark E. Anderson,et al.  Sudden Cardiac Death Prediction and Prevention: Report From a National Heart, Lung, and Blood Institute and Heart Rhythm Society Workshop , 2010, Circulation.

[29]  Dan W Rettmann,et al.  Accurate and Objective Infarct Sizing by Contrast-enhanced Magnetic Resonance Imaging in a Canine Myocardial Infarction Model , 2022 .

[30]  Elena Arbelo,et al.  Noninvasive identification of ventricular tachycardia-related conducting channels using contrast-enhanced magnetic resonance imaging in patients with chronic myocardial infarction: comparison of signal intensity scar mapping and endocardial voltage mapping. , 2011, Journal of the American College of Cardiology.

[31]  Lippincott Williams Wilkins,et al.  ACC/AHA/ESC 2006 Guidelines for Management of Patients With Ventricular Arrhythmias and the Prevention of Sudden Cardiac Death—Executive Summary , 2006 .

[32]  Nicolas P. Smith,et al.  Investigating a Novel Activation-Repolarisation Time Metric to Predict Localised Vulnerability to Reentry Using Computational Modelling , 2016, PloS one.

[33]  Natalia A Trayanova,et al.  A feasibility study of arrhythmia risk prediction in patients with myocardial infarction and preserved ejection fraction. , 2016, Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology.

[34]  M. Josephson,et al.  High-Resolution Mapping of Postinfarction Reentrant Ventricular Tachycardia: Electrophysiological Characterization of the Circuit. , 2016, Circulation.

[35]  N. Trayanova,et al.  Exploring susceptibility to atrial and ventricular arrhythmias resulting from remodeling of the passive electrical properties in the heart: a simulation approach , 2014, Front. Physiol..

[36]  Stefan Dhein,et al.  Remodeling of cardiac passive electrical properties and susceptibility to ventricular and atrial arrhythmias , 2014, Front. Physiol..

[37]  Katherine C. Wu,et al.  Image-based left ventricular shape analysis for sudden cardiac death risk stratification. , 2013, Heart rhythm.