Non-invasive epicardial and endocardial electrocardiographic imaging for scar-related ventricular tachycardia

Evaluation of five methods for the interpolation of bad leads in the solution of the inverse electrocardiography problem

Objective.This study aims to assess the sensitivity of epicardial potential-based electrocardiographic imaging (ECGI) to the removal or interpolation of bad leads.Approach.We utilized experimental data from two distinct centers. Langendorff-perfused pig (n= 2) and dog (n= 2) hearts were suspended in a human torso-shaped tank and paced from the ventricles. Six different bad lead configurations were designed based on clinical experience. Five interpolation methods were applied to estimate the missing data. Zero-order Tikhonov regularization was used to solve the inverse problem for complete data, data with removed bad leads, and interpolated data. We assessed the quality of interpolated ECG signals and ECGI reconstructions using several metrics, comparing the performance of interpolation methods and the impact of bad lead removal versus interpolation on ECGI.Main results.The performance of ECG interpolation strongly correlated with ECGI reconstruction. The hybrid method exhibited the best performance among interpolation techniques, followed closely by the inverse-forward and Kriging methods. Bad leads located over high amplitude/high gradient areas on the torso significantly impacted ECGI reconstructions, even with minor interpolation errors. The choice between removing or interpolating bad leads depends on the location of missing leads and confidence in interpolation performance. If uncertainty exists, removing bad leads is the safer option, particularly when they are positioned in high amplitude/high gradient regions. In instances where interpolation is necessary, the inverse-forward and Kriging methods, which do not require training, are recommended.Significance.This study represents the first comprehensive evaluation of the advantages and drawbacks of interpolating versus removing bad leads in the context of ECGI, providing valuable insights into ECGI performance.

Understanding the Utility of Endocardial Electrocardiographic Imaging in Epi-Endocardial Mapping of 3D Reentrant Circuits

Background

Studies of VT mechanisms are largely based on a 2D portrait of reentrant circuits on one surface of the heart. This oversimplifies the 3D circuit that involves the depth of the myocardium. Simultaneous epicardial and endocardial (epi-endo) mapping was shown to facilitate a 3D delineation of VT circuits, which is however difficult via invasive mapping.

Objective

This study investigates the capability of noninvasive epicardial-endocardial electrocardiographic imaging (ECGI) to elucidate the 3D construct of VT circuits, emphasizing the differentiation of epicardial, endocardial, and intramural circuits and to determine the proximity of mid-wall exits to the epicardial or endocardial surfaces.

Methods

120-lead ECGs of VT in combination with subject-specific heart-torso geometry are used to compute unipolar electrograms (CEGM) on ventricular epicardium and endocardia. Activation isochrones are constructed, and the percentage of activation within VT cycle length is calculated on each surface. This classifies VT circuits into 2D (surface only), uniform transmural, nonuniform transmural, and mid-myocardial (focal on surfaces). Furthermore, the endocardial breakthrough time was accurately measured using Laplacian eigenmaps, and by correlating the delay time of the epi-endo breakthroughs, the relative distance of a mid-wall exit to the epicardium or the endocardium surfaces was identified.

Results

We analyzed 23 simulated and in-vivo VT circuits on post-infarction porcine hearts. In simulated circuits, ECGI classified 21% as 2D and 78% as 3D: 82.6% of these were correctly classified. The relative timing between epicardial and endocardial breakthroughs was correctly captured across all cases. In in-vivo circuits, ECGI classified 25% as 2D and 75% as 3D: in all cases, circuit exits and entrances were consistent with potential critical isthmus delineated from combined LGE-MRI and catheter mapping data.

Conclusions

ECGI epi-endo mapping has the potential for fast delineation of 3D VT circuits, which may augment detailed catheter mapping for VT ablation.

Basis and applicability of noninvasive inverse electrocardiography: a comparison between cardiac source models

The body surface electrocardiogram (ECG) is a direct result of electrical activity generated by the myocardium. Using the body surface ECGs to reconstruct cardiac electrical activity is called the inverse problem of electrocardiography. The method to solve the inverse problem depends on the chosen cardiac source model to describe cardiac electrical activity. In this paper, we describe the theoretical basis of two inverse methods based on the most commonly used cardiac source models: the epicardial potential model and the equivalent dipole layer model. We discuss similarities and differences in applicability, strengths and weaknesses and sketch a road towards improved inverse solutions by targeted use, sequential application or a combination of the two methods.

Innovations in ventricular tachycardia ablation

Catheter ablation of ventricular arrhythmias (VAs) has evolved significantly over the past decade and is currently a well-established therapeutic option. Technological advances and improved understanding of VA mechanisms have led to tremendous innovations in VA ablation. The purpose of this review article is to provide an overview of current innovations in VA ablation. Mapping techniques, such as ultra-high density mapping, isochronal late activation mapping, and ripple mapping, have provided improved arrhythmogenic substrate delineation and potential procedural success while limiting duration of ablation procedure and potential hemodynamic compromise. Besides, more advanced mapping and ablation techniques such as epicardial and intramyocardial ablation approaches have allowed operators to more precisely target arrhythmogenic substrate. Moreover, advances in alternate energy sources, such as electroporation, as well as stereotactic radiation therapy have been proposed to be effective and safe. New catheters, such as the lattice and the saline-enhanced radiofrequency catheters, have been designed to provide deeper and more durable tissue ablation lesions compared to conventional catheters. Contact force optimization and baseline impedance modulation are important tools to optimize VT radiofrequency ablation and improve procedural success. Furthermore, advances in cardiac imaging, specifically cardiac MRI, have great potential in identifying arrhythmogenic substrate and evaluating ablation success. Overall, VA ablation has undergone significant advances over the past years. Innovations in VA mapping techniques, alternate energy source, new catheters, and utilization of cardiac imaging have great potential to improve overall procedural safety, hemodynamic stability, and procedural success.

Assessing Noninvasive Delineation of Low-Voltage Zones Using ECG Imaging in Patients With Structural Heart Disease

OBJECTIVES

This study sought to assess the association between electrocardiographic imaging (ECGI) parameters and voltage from simultaneous electroanatomic mapping (EAM).

BACKGROUND

ECGI offers noninvasive assessment of electrophysiologic features relevant for mapping ventricular arrhythmia and its substrate, but the accuracy of ECGI in the delineation of scar is unclear.

METHODS

Sixteen patients with structural heart disease underwent simultaneous ECGI (CardioInsight, Medtronic) and contact EAM (CARTO, Biosense-Webster) during ventricular tachycardia catheter ablation, with 7 mapped epicardially. ECGI and EAM geometries were coregistered using anatomic landmarks. ECGI points were paired to the closest site on the EAM within 10 mm. The association between EAM voltage and ECGI features from reconstructed epicardial unipolar electrograms was assessed by mixed-effects regression models. The classification of low-voltage regions was performed using receiver-operating characteristic analysis.

RESULTS

A total of 9,541 ECGI points (median: 596; interquartile range: 377-737 across patients) were paired to an EAM site. Epicardial EAM voltage was associated with ECGI features of signal fractionation and local repolarization dispersion (N = 7; P < 0.05), but they poorly classified sites with bipolar voltage of <1.5 mV or <0.5 mV thresholds (median area under the curve across patients: 0.50-0.62). No association was found between bipolar EAM voltage and low-amplitude reconstructed epicardial unipolar electrograms or ECGI-derived bipolar electrograms. Similar results were found in the combined cohort (n = 16), including endocardial EAM voltage compared to epicardial ECGI features (n = 9).

CONCLUSIONS

Despite a statistically significant association between ECGI features and EAM voltage, the accuracy of the delineation of low-voltage zones was modest. This may limit ECGI use for pr-procedural substrate analysis in ventricular tachycardia ablation, but it could provide value in risk assessment for ventricular arrhythmias.

Spectral Analysis of Tissue Displacement for Cardiac Activation Mapping: Ex Vivo Working Heart and In Vivo Study

Characterizing myocardial activation is of major interest for understanding the underlying mechanism of cardiac arrhythmias. Electromechanical wave imaging (EWI) is an ultrafast ultrasound-based method used to map the propagation of the local contraction triggered by electrical activation of the heart. This study introduces a novel way to characterize cardiac activation based on the time evolution of the instantaneous frequency content of the cardiac tissue displacement curves. The first validation of this method was performed on an ex vivo dataset of 36 acquisitions acquired from two working heart models in paced rhythms. It was shown that the activation mapping described by spectral analysis of interframe displacement is similar to the standard EWI method based on zero-crossing of interframe strain. An average median error of 3.3 ms was found in the ex vivo dataset between the activation maps obtained with the two methods. The feasibility of mapping cardiac activation by EWI was then investigated on two open-chest pigs during sinus and paced rhythms in a pilot trial of cardiac mapping with an intracardiac probe. Seventy-five acquisitions were performed with reasonable stability and analyzed with the novel algorithm to map cardiac contraction propagation in the left ventricle (LV). Sixty-one qualitatively continuous isochrones were successfully computed based on this method. The region of contraction onset was coherently described while pacing in the imaging plane. These findings highlight the potential of implementing EWI acquisition on intracardiac probes and emphasize the benefit of performing short time-frequency analysis of displacement data to characterize cardiac activation in vivo.

Ajmaline-Induced Abnormalities in Brugada Syndrome: Evaluation With ECG Imaging

Background The rate of sudden cardiac death (SCD) in Brugada syndrome (BrS) is ≈1%/y. Noninvasive electrocardiographic imaging is a noninvasive mapping system that has a role in assessing BrS depolarization and repolarization abnormalities. This study aimed to analyze electrocardiographic imaging parameters during ajmaline test (AJT). Methods and Results All consecutive epicardial maps of the right ventricle outflow tract (RVOT-EPI) in BrS with CardioInsight were retrospectively analyzed. (1) RVOT-EPI activation time (RVOT-AT); (2) RVOT-EPI recovery time, and (3) RVOT-EPI activation-recovery interval (RVOT-ARI) were calculated. ∆RVOT-AT, ∆RVOT-EPI recovery time, and ∆RVOT-ARI were defined as the difference in parameters before and after AJT. SCD-BrS patients were defined as individuals presenting a history of aborted SCD. Thirty-nine patients with BrS were retrospectively analyzed and 12 patients (30.8%) were SCD-BrS. After AJT, an increase in both RVOT-AT [105.9 milliseconds versus 65.8 milliseconds, P<0.001] and RVOT-EPI recovery time [403.4 milliseconds versus 365.7 milliseconds, P<0.001] was observed. No changes occurred in RVOT-ARI [297.5 milliseconds versus 299.9 milliseconds, P=0.7]. Before AJT no differences were observed between SCD-BrS and non SCD-BrS in RVOT-AT, RVOT-EPI recovery time, and RVOT-ARI (P=0.9, P=0.91, P=0.86, respectively). Following AJT, SCD-BrS patients showed higher RVOT-AT, higher ∆RVOT-AT, lower RVOT-ARI, and lower ∆RVOT-ARI (P<0.001, P<0.001, P=0.007, P=0.002, respectively). At the univariate logistic regression, predictors of SCD-BrS were the following: RVOT-AT after AJT (specificity: 0.74, sensitivity 1.00, area under the curve 0.92); ∆RVOT-AT (specificity: 0.74, sensitivity 0.92, area under the curve 0.86); RVOT-ARI after AJT (specificity 0.96, sensitivity 0.58, area under the curve 0.79), and ∆RVOT-ARI (specificity 0.85, sensitivity 0.67, area under the curve 0.76). Conclusions Noninvasive electrocardiographic imaging can be useful in evaluating the results of AJT in BrS.

Reducing Line-of-block Artifacts in Cardiac Activation Maps Estimated Using ECG Imaging: A Comparison of Source Models and Estimation Methods

OBJECTIVE

To investigate cardiac activation maps estimated using electrocardiographic imaging and to find methods reducing line-of-block (LoB) artifacts, while preserving real LoBs.

METHODS

Body surface potentials were computed for 137 simulated ventricular excitations. Subsequently, the inverse problem was solved to obtain extracellular potentials (EP) and transmembrane voltages (TMV). From these, activation times (AT) were estimated using four methods and compared to the ground truth. This process was evaluated with two cardiac mesh resolutions. Factors contributing to LoB artifacts were identified by analyzing the impact of spatial and temporal smoothing on the morphology of source signals.

RESULTS

AT estimation using a spatiotemporal derivative performed better than using a temporal derivative. Compared to deflection-based AT estimation, correlation-based methods were less prone to LoB artifacts but performed worse in identifying real LoBs. Temporal smoothing could eliminate artifacts for TMVs but not for EPs, which could be linked to their temporal morphology. TMVs led to more accurate ATs on the septum than EPs. Mesh resolution had a negligible effect on inverse reconstructions, but small distances were important for cross-correlation-based estimation of AT delays.

CONCLUSION

LoB artifacts are mainly caused by the inherent spatial smoothing effect of the inverse reconstruction. Among the configurations evaluated, only deflection-based AT estimation in combination with TMVs and strong temporal smoothing can prevent LoB artifacts, while preserving real LoBs.

SIGNIFICANCE

Regions of slow conduction are of considerable clinical interest and LoB artifacts observed in non-invasive ATs can lead to misinterpretations. We addressed this problem by identifying factors causing such artifacts and methods to reduce them.

Combining endocardial mapping and electrocardiographic imaging (ECGI) for improving PVC localization: A feasibility study

INTRODUCTION

Accurate reconstruction of cardiac activation wavefronts is crucial for clinical diagnosis, management, and treatment of cardiac arrhythmias. Furthermore, reconstruction of activation profiles within the intramural myocardium has long been impossible because electrical mapping was only performed on the endocardial surface. Recent advancements in electrocardiographic imaging (ECGI) have made endocardial and epicardial activation mapping possible. We propose a novel approach to use both endocardial and epicardial mapping in a combined approach to reconstruct intramural activation times.

OBJECTIVE

To implement and validate a combined epicardial/endocardial intramural activation time reconstruction technique.

METHODS

We used 11 simulations of ventricular activation paced from sites throughout myocardial wall and extracted endocardial and epicardial activation maps at approximate clinical resolution. From these maps, we interpolated the activation times through the myocardium using thin-plate-spline radial basis functions. We evaluated activation time reconstruction accuracy using root-mean-squared error (RMSE) of activation times and the percent of nodes within 1 ms of the ground truth.

RESULTS

Reconstructed intramural activation times showed an RMSE and percentage of nodes within 1 ms of the ground truth simulations of 3 ms and 70%, respectively. In the worst case, the RMSE and percentage of nodes were 4 ms and 60%, respectively.

CONCLUSION

We showed that a simple, yet effective combination of clinical endocardial and epicardial activation maps can accurately reconstruct intramural wavefronts. Furthermore, we showed that this approach provided robust reconstructions across multiple intramural stimulation sites.

Perioperative Imaging to Guide Epicardial Mapping and Ablation

Accessing the epicardial space without a sternotomy or a surgical pericardial window to treat ventricular arrhythmias in Chagas disease became a medical necessity in South America. Since the introduction of the dry percutaneous epicardial access approach, epicardial access has been standard procedure for management of ventricular arrhythmias in ischemic and nonischemic cardiomyopathies and atrioventricular accessory pathways after failed conventional endocardial ablation. Understanding the epicardial space and neighboring structures has become an important subject of teachings in electrophysiology. The evolution of complex ablation procedures to treat atrial and ventricular arrhythmias and device interventions to prevent cardioembolic stroke requires thorough understanding of pericardial anatomy.

Electrocardiographic imaging for cardiac arrhythmias and resynchronization therapy

Use of the 12-lead electrocardiogram (ECG) is fundamental for the assessment of heart disease, including arrhythmias, but cannot always reveal the underlying mechanism or the location of the arrhythmia origin. Electrocardiographic imaging (ECGi) is a non-invasive multi-lead ECG-type imaging tool that enhances conventional 12-lead ECG. Although it is an established technology, its continuous development has been shown to assist in arrhythmic activation mapping and provide insights into the mechanism of cardiac resynchronization therapy (CRT). This review addresses the validity, reliability, and overall feasibility of ECGi for use in a diverse range of arrhythmias. A systematic search limited to full-text human studies published in peer-reviewed journals was performed through Medline via PubMed, using various combinations of three key concepts: ECGi, arrhythmia, and CRT. A total of 456 studies were screened through titles and abstracts. Ultimately, 42 studies were included for literature review. Evidence to date suggests that ECGi can be used to provide diagnostic insights regarding the mechanistic basis of arrhythmias and the location of arrhythmia origin. Furthermore, ECGi can yield valuable information to guide therapeutic decision-making, including during CRT. Several studies have used ECGi as a diagnostic tool for atrial and ventricular arrhythmias. More recently, studies have tested the value of this technique in predicting outcomes of CRT. As a non-invasive method for assessing cardiovascular disease, particularly arrhythmias, ECGi represents a significant advancement over standard procedures in contemporary cardiology. Its full potential has yet to be fully explored.

Mechanisms of Arrhythmogenicity in Hypertrophic Cardiomyopathy: Insight From Non-invasive Electrocardiographic Imaging

Background

Mechanisms of arrhythmogenicity in hypertrophic cardiomyopathy (HCM) are not well understood.

Objective

To characterize an electrophysiological substrate of HCM in comparison to ischemic cardiomyopathy (ICM), or healthy individuals.

Methods

We conducted a prospective case-control study. The study enrolled HCM patients at high risk for ventricular tachyarrhythmia (VT) [n = 10; age 61 ± 9 years; left ventricular ejection fraction (LVEF) 60 ± 9%], and three comparison groups: healthy individuals (n = 10; age 28 ± 6 years; LVEF > 70%), ICM patients with LV hypertrophy (LVH) and known VT (n = 10; age 64 ± 9 years; LVEF 31 ± 15%), and ICM patients with LVH and no known VT (n = 10; age 70 ± 7 years; LVEF 46 ± 16%). All participants underwent 12-lead ECG, cardiac CT or MRI, and 128-electrode body surface mapping (BioSemi ActiveTwo, Netherlands). Non-invasive voltage and activation maps were reconstructed using the open-source SCIRun (University of Utah) inverse problem-solving environment.

Results

In the epicardial basal anterior segment, HCM patients had the greatest ventricular activation dispersion [16.4 ± 5.5 vs. 13.1 ± 2.7 (ICM with VT) vs. 13.8 ± 4.3 (ICM no VT) vs. 8.1 ± 2.4 ms (Healthy); P = 0.0007], the largest unipolar voltage [1094 ± 211 vs. 934 ± 189 (ICM with VT) vs. 898 ± 358 (ICM no VT) vs. 842 ± 90 μV (Healthy); P = 0.023], and the greatest voltage dispersion [median (interquartile range) 215 (161–281) vs. 189 (143–208) (ICM with VT) vs. 158 (109–236) (ICM no VT) vs. 110 (106–168) μV (Healthy); P = 0.041]. Differences were also observed in other endo-and epicardial basal and apical segments.

Conclusion

HCM is characterized by a greater activation dispersion in basal segments, a larger voltage, and a larger voltage dispersion through LV.

Clinical Trial Registration

www.clinicaltrials.gov Unique identifier: NCT02806479.

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

The estimation of patient-specific tissue properties in the form of model parameters is important for personalized physiological models. Because tissue properties are spatially varying across the underlying geometrical model, it presents a significant challenge of high-dimensional (HD) optimization at the presence of limited measurement data. A common solution to reduce the dimension of the parameter space is to explicitly partition the geometrical mesh. In this paper, we present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the generative code of HD parameters. We further utilize VAE-encoded knowledge about the generative code to guide the exploration of the search space. The presented method is applied to estimating tissue excitability in a cardiac electrophysiological model in a range of synthetic and real-data experiments, through which we demonstrate its improved accuracy and substantially reduced computational cost in comparison to existing methods that rely on geometry-based reduction of the HD parameter space.

How personalized heart modeling can help treatment of lethal arrhythmias: A focus on ventricular tachycardia ablation strategies in post-infarction patients

Precision Cardiology is a targeted strategy for cardiovascular disease prevention and treatment that accounts for individual variability. Computational heart modeling is one of the novel approaches that have been developed under the umbrella of Precision Cardiology. Personalized computational modeling of patient hearts has made strides in the development of models that incorporate the individual geometry and structure of the heart as well as other patient-specific information. Of these developments, one of the potentially most impactful is the research aimed at noninvasively predicting the targets of ablation of lethal arrhythmia, ventricular tachycardia (VT), using patient-specific models. The approach has been successfully applied to patients with ischemic cardiomyopathy in proof-of-concept studies. The goal of this paper is to review the strategies for computational VT ablation guidance in ischemic cardiomyopathy patients, from model developments to the intricacies of the actual clinical application. To provide context in describing the road these computational modeling applications have undertaken, we first review the state of the art in VT ablation in the clinic, emphasizing the benefits that personalized computational prediction of ablation targets could bring to the clinical electrophysiology practice. This article is characterized under: Analytical and Computational Methods > Computational Methods Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models Translational, Genomic, and Systems Medicine > Translational Medicine.

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

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.