Noninvasive Mapping and Electrocardiographic Imaging in Atrial and Ventricular Arrhythmias (CardioInsight)

A Tool to Integrate Electrophysiological Mapping for Cardiac Radioablation of Ventricular Tachycardia

Purpose

Cardiac radioablation is an emerging therapy for recurrent ventricular tachycardia. Electrophysiology (EP) data, including electroanatomic maps (EAM) and electrocardiographic imaging (ECGI), provide crucial information for defining the arrhythmogenic target volume. The absence of standardized workflows and software tools to integrate the EP maps into a radiation planning system limits their use. This study developed a comprehensive software tool to enable efficient utilization of the mapping for cardiac radioablation treatment planning.

Methods and Materials

The tool, HeaRTmap, is a Python-scripted plug-in module on the open-source 3D Slicer software platform. HeaRTmap is able to import EAM and ECGI data and visualize the maps in 3D Slicer. The EAM is translated into a 3D space by registration with cardiac magnetic resonance images (MRI) or computed tomography (CT). After the scar area is outlined on the mapping surface, the tool extracts and extends the annotated patch into a closed surface and converts it into a structure set associated with the anatomic images. The tool then exports the structure set and the images as The Digital Imaging and Communications in Medicine Standard in Radiotherapy for a radiation treatment planning system to import. Overlapping the scar structure on simulation CT, a transmural target volume is delineated for treatment planning.

Results

The tool has been used to transfer Ensite NavX EAM data into the Varian Eclipse treatment planning system in radioablation on 2 patients with ventricular tachycardia. The ECGI data from CardioInsight was retrospectively evaluated using the tool to derive the target volume for a patient with left ventricular assist device, showing volumetric matching with the clinically used target with a Dice coefficient of 0.71.

Conclusions

HeaRTmap smoothly fuses EP information from different mapping systems with simulation CT for accurate definition of radiation target volume. The efficient integration of EP data into treatment planning potentially facilitates the study and adoption of the technique.

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.

Electrocardiographic imaging in the atria

The inverse problem of electrocardiography or electrocardiographic imaging (ECGI) is a technique for reconstructing electrical information about cardiac surfaces from noninvasive or non-contact recordings. ECGI has been used to characterize atrial and ventricular arrhythmias. Although it is a technology with years of progress, its development to characterize atrial arrhythmias is challenging. Complications can arise when trying to describe the atrial mechanisms that lead to abnormal propagation patterns, premature or tachycardic beats, and reentrant arrhythmias. This review addresses the various ECGI methodologies, regularization methods, and post-processing techniques used in the atria, as well as the context in which they are used. The current advantages and limitations of ECGI in the fields of research and clinical diagnosis of atrial arrhythmias are outlined. In addition, areas where ECGI efforts should be concentrated to address the associated unsatisfied needs from the atrial perspective are discussed.

Evaluation of photogrammetry for medical application in cardiology

Background: In the field of medicine, photogrammetry has played for long time a marginal role due to the significant amount of work required that made it impractical for an extended medical use. Developments in digital photogrammetry occurred in the recent years, that have steadily increased the interest and application of this technique. The present study aims to compare photogrammetry reconstruction of heart with computed tomography (CT) as a reference. Methods: The photogrammetric reconstructions of digital images from ECG imaging derived images were performed. In particular, the ventricles of 15 patients with Brugada syndrome were reconstructed by using the free Zephyr Lite software. In order to evaluate the accuracy of the technique, measurements on the reconstructions were compared to patient-specific CT scan imported in ECG imaging software UZBCIT. Result: The results showed that digital photogrammetry in the context of ventricle reconstruction is feasible. The photogrammetric derived measurements of ventricles were not statistically different from CT scan measurements. Furthermore, the analysis showed high correlation of photogrammetry reconstructions with CT scan and a correlation coefficient close to 1. Conclusion: It is possible to reproduce digital objects by photogrammetry if the process described in this study is performed. The reconstruction of the ventricles from CT scan was very close to the values of the respective photogrammetric reconstruction.

Development of a 3D printed surgical guide for Brugada syndrome substrate ablation

Background

Brugada syndrome (BrS) is a disease associated with ventricular arrhythmias and sudden cardiac death. Epicardial ablation has demonstrated high therapeutic efficacy in preventing ventricular arrhythmias. The purpose of this research is to define a workflow to create a patient-specific 3D-printed tool to be used as a surgical guide for epicardial ablation in BrS.

Methods

Due to their mechanical properties and biocompatibility, the MED625FLX and TPU95A were used for cardiac 3D surgical guide printing. ECG imaging was used to define the target region on the right ventricular outflow tract (RVOT). CT scan imaging was used to design the model based on patient anatomy. A 3D patient-specific heart phantom was also printed for fitting test. Sterilization test was finally performed.

Results

3D printed surgical models with both TPU95A and MED625FLX models were in agreement with pre-specified imputed measurements. The phantom test showed retention of shape and correct fitting of the surgical tool to the reproduced phantom anatomy, as expected, for both materials. The surgical guide adapted to both the RVOT and the left anterior descending artery. Two of the 3D models produced in MED265FLX showed damage due to the sterilization process.

Conclusions

A 3D printed patient-specific surgical guide for epicardial substrate ablation in BrS is feasible if a specific workflow is followed. The design of the 3D surgical guide ensures proper fitting on the heart phantom with good stability. Further investigations for clinical use are eagerly awaited.

Solving Inverse Electrocardiographic Mapping Using Machine Learning and Deep Learning Frameworks

Electrocardiographic imaging (ECGi) reconstructs electrograms at the heart's surface using the potentials recorded at the body's surface. This is called the inverse problem of electrocardiography. This study aimed to improve on the current solution methods using machine learning and deep learning frameworks. Electrocardiograms were simultaneously recorded from pigs' ventricles and their body surfaces. The Fully Connected Neural network (FCN), Long Short-term Memory (LSTM), Convolutional Neural Network (CNN) methods were used for constructing the model. A method is developed to align the data across different pigs. We evaluated the method using leave-one-out cross-validation. For the best result, the overall median of the correlation coefficient of the predicted ECG wave was 0.74. This study demonstrated that a neural network can be used to solve the inverse problem of ECGi with relatively small datasets, with an accuracy compatible with current standard methods.

Generalization and Regularization for Inverse Cardiac Estimators

Electrocardiographic Imaging (ECGI) aims to estimate the intracardiac potentials noninvasively, hence allowing the clinicians to better visualize and understand many arrhythmia mechanisms. Most of the estimators of epicardial potentials use a signal model based on an estimated spatial transfer matrix together with Tikhonov regularization techniques, which works well specially in simulations, but it can give limited accuracy in some real data. Based on the quasielectrostatic potential superposition principle, we propose a simple signal model that supports the implementation of principled out-of-sample algorithms for several of the most widely used regularization criteria in ECGI problems, hence improving the generalization capabilities of several of the current estimation methods. Experiments on simple cases (cylindrical and Gaussian shapes scrutinizing fast and slow changes, respectively) and on real data (examples of torso tank measurements available from Utah University, and an animal torso and epicardium measurements available from Maastricht University, both in the EDGAR public repository) show that the superposition-based out-of-sample tuning of regularization parameters promotes stabilized estimation errors of the unknown source potentials, while slightly increasing the re-estimation error on the measured data, as natural in non-overfitted solutions. The superposition signal model can be used for designing adequate out-of-sample tuning of Tikhonov regularization techniques, and it can be taken into account when using other regularization techniques in current commercial systems and research toolboxes on ECGI.

Recent advances in the tools available for atrial fibrillation ablation

INTRODUCTION

Atrial fibrillation (AF) is the commonest arrhythmia in clinical practice with significant detrimental health impacts. Much effort has been spent in mapping AF, determine its triggers and drivers, and how to develop tools to eliminate these triggers.

AREAS COVERED

In this state of-the-art review article, we aim to highlight the recent techniques in catheter-based management of Atrial Fibrillation; including new advancements either in the catheter design or the software used. This includes a comprehensive summary of the most recent tools used in AF mapping and subsequent ablation.

EXPERT OPINION

Electrical isolation of the pulmonary veins has been developed and established as the cornerstone in AF ablation with good results in patients with paroxysmal AF (PAF) whilst new ablation tools are aimed at streamlining the procedure. However, the quest for persistent AF (PeAF) remains. The future of AF ablation, we believe, lies in identifying AF drivers by means of the new developing mapping tools and altering their electrical properties in a safe, reproducible, and effective manner.

Treatment of brief episodes of highly symptomatic supraventricular and ventricular arrhythmias: a methodological review

INTRODUCTION

Patients with brief arrhythmias are a challenging group to treat effectively with catheter ablation. Current standard approaches for the localization and treatment of brief arrhythmias suffer from several limitations, including the lack of spatiotemporal stability and adequate resolution. Recently, novel methods became available that open new perspectives and can be implemented both on the atrial and ventricular level to approach the diagnosis and treatment of these arrhythmias.

AREAS COVERED

In this paper, we demonstrate in each section a novel mapping modality that has a potential to approach arrhythmias considered unmappable in the past. After describing the method, we focused on the most important features of each system that makes mapping of short arrhythmias feasible. At the end of each section, we gave a short overview about necessary developments to improve the utility of these systems in the near future.

EXPERT OPINION

Treating brief episodes of tachycardias remains a challenge and can cause significant frustration for electrophysiologists. Although the broadening of the indication is clearly visible, currently available sequential mapping techniques often fail to map short-lived arrhythmias. New beneficial technological features permit the mapping of these previously considered unmappable arrhythmias, and offer a new perspective in their management.

Body Surface Potential Mapping: Contemporary Applications and Future Perspectives

Body surface potential mapping (BSPM) is a noninvasive modality to assess cardiac bioelectric activity with a rich history of practical applications for both research and clinical investigation. BSPM provides comprehensive acquisition of bioelectric signals across the entire thorax, allowing for more complex and extensive analysis than the standard electrocardiogram (ECG). Despite its advantages, BSPM is not a common clinical tool. BSPM does, however, serve as a valuable research tool and as an input for other modes of analysis such as electrocardiographic imaging and, more recently, machine learning and artificial intelligence. In this report, we examine contemporary uses of BSPM, and provide an assessment of its future prospects in both clinical and research environments. We assess the state of the art of BSPM implementations and explore modern applications of advanced modeling and statistical analysis of BSPM data. We predict that BSPM will continue to be a valuable research tool, and will find clinical utility at the intersection of computational modeling approaches and artificial intelligence.

Abnormalities in sodium current and calcium homoeostasis as drivers of arrhythmogenesis in hypertrophic cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is a common inherited monogenic disease with a prevalence of 1/500 in the general population, representing an important cause of arrhythmic sudden cardiac death (SCD), heart failure, and atrial fibrillation in the young. HCM is a global condition, diagnosed in >50 countries and in all continents. HCM affects people of both sexes and various ethnic and racial origins, with similar clinical course and phenotypic expression. The most unpredictable and devastating consequence of HCM is represented by arrhythmic SCD, most commonly caused by sustained ventricular tachycardia or ventricular fibrillation. Indeed, HCM represents one of the main causes of arrhythmic SCD in the young, with a marked preference for children and adults <30 years. SCD is most prevalent in patients with paediatric onset of HCM but may occur at any age. However, risk is substantially lower after 60 years, suggesting that the potential for ventricular tachyarrhythmias is mitigated by ageing. SCD had been linked originally to sports and vigorous activity in HCM patients. However, it is increasingly clear that the majority of events occurs at rest or during routine daily occupations, suggesting that triggers are far from consistent. In general, the pathophysiology of SCD in HCM remains unresolved. While the pathologic and physiologic substrates abound and have been described in detail, specific factors precipitating ventricular tachyarrhythmias are still unknown. SCD is a rare phenomenon in HCM cohorts (<1%/year) and attempts to identify patients at risk, while generating clinically useful algorithms for primary prevention, remain very inaccurate on an individual basis. One of the reasons for our limited understanding of these phenomena is that limited translational research exists in the field, while most efforts have focused on clinical markers of risk derived from pathology, instrumental patient evaluation, and imaging. Specifically, few studies conducted in animal models and human samples have focused on targeting the cellular mechanisms of arrhythmogenesis in HCM, despite potential implications for therapeutic innovation and SCD prevention. These studies found that altered intracellular Ca2+ homoeostasis and increased late Na+ current, leading to an increased likelihood of early and delayed after-depolarizations, contribute to generate arrhythmic events in diseased cardiomyocytes. As an array of novel experimental opportunities have emerged to investigate these mechanisms, including novel 'disease-in-the-dish' cellular models with patient-specific induced pluripotent stem cell-derived cardiomyocytes, important gaps in knowledge remain. Accordingly, the aim of the present review is to provide a contemporary reappraisal of the cellular basis of SCD-predisposing arrhythmias in patients with HCM and discuss the implications for risk stratification and management.

Artificial intelligence in the diagnosis and management of arrhythmias

The field of cardiac electrophysiology (EP) had adopted simple artificial intelligence (AI) methodologies for decades. Recent renewed interest in deep learning techniques has opened new frontiers in electrocardiography analysis including signature identification of diseased states. Artificial intelligence advances coupled with simultaneous rapid growth in computational power, sensor technology, and availability of web-based platforms have seen the rapid growth of AI-aided applications and big data research. Changing lifestyles with an expansion of the concept of internet of things and advancements in telecommunication technology have opened doors to population-based detection of atrial fibrillation in ways, which were previously unimaginable. Artificial intelligence-aided advances in 3D cardiac imaging heralded the concept of virtual hearts and the simulation of cardiac arrhythmias. Robotics, completely non-invasive ablation therapy, and the concept of extended realities show promise to revolutionize the future of EP. In this review, we discuss the impact of AI and recent technological advances in all aspects of arrhythmia care.

Surface unipolar electrogram characteristics to predict site of origin of outflow tract arrhythmias using noninvasive mapping

BACKGROUND

Noninvasive electroanatomic mapping (NIEAM) demonstrate patterns of depolarization that are useful in identifying the chamber of origin (COO) in outflow tract ventricular arrhythmias (OTVA). However, its use in predicting exact site of origin (SOO) has not yet been validated.

METHODS

NIEAMs (CardioInsight, Medtronic) from 40 patients (age 62.5 ± 2.6) undergoing ablation for OTVA were reviewed for diagnostic accuracy in predicting the SOO. Earliest arrhythmia breakout and directionality of earliest instantaneous unipolar electrograms (uEGMs) on NIEAMs were evaluated subjectively by two observers for quality and amplitude. Sites with most negative earliest uEGMs on right and left ventricular outflow tracts, as well as epicardial surface were manually identified. Using NIEAM-based activation timing of the lateral mitral annulus and basal septum COO was identified for each OTVA. Predictions of SOO using NIEAMs was compared with true SOO from invasive study. NIEAMs SOO predictions were compared with subjective 12 lead electrocardiogram (ECG) review by two observers.

RESULTS

Review of arrhythmia breakout and signal directionality had poor diagnostic value in predicting SOO in OTVA (50.6% and 49.4%, 56.6% and 43.4%, respectively) and underperformed compared with ECG interpretation (59.1% and 80.5%). After excluding uEGMs with poor characteristics, the uEGM with most negative amplitude at the COO was predictive of the true SOO with 96.4% sensitivity and specificity.

CONCLUSION

We propose a stepwise approach when interpreting NIEAMs for OTVA where patterns of activation are evaluated first to determine the COO, followed by identification of the site with most negative amplitude instantaneous uEGM to determine SOO.

Spatial-Temporal Signals and Clinical Indices in Electrocardiographic Imaging (II): Electrogram Clustering and T-wave Alternans

During the last years, attention and controversy have been present for the first commercially available equipment being used in Electrocardiographic Imaging (ECGI), a new cardiac diagnostic tool which opens up a new field of diagnostic possibilities. Previous knowledge and criteria of cardiologists using intracardiac Electrograms (EGM) should be revisited from the newly available spatial-temporal potentials, and digital signal processing should be readapted to this new data structure. Aiming to contribute to the usefulness of ECGI recordings in the current knowledge and methods of cardiac electrophysiology, we previously presented two results: First, spatial consistency can be observed even for very basic cardiac signal processing stages (such as baseline wander and low-pass filtering); second, useful bipolar EGMs can be obtained by a digital processing operator searching for the maximum amplitude and including a time delay. In addition, this work aims to demonstrate the functionality of ECGI for cardiac electrophysiology from a twofold view, namely, through the analysis of the EGM waveforms, and by studying the ventricular repolarization properties. The former is scrutinized in terms of the clustering properties of the unipolar an bipolar EGM waveforms, in control and myocardial infarction subjects, and the latter is analyzed using the properties of T-wave alternans (TWA) in control and in Long-QT syndrome (LQTS) example subjects. Clustered regions of the EGMs were spatially consistent and congruent with the presence of infarcted tissue in unipolar EGMs, and bipolar EGMs with adequate signal processing operators hold this consistency and yielded a larger, yet moderate, number of spatial-temporal regions. TWA was not present in control compared with an LQTS subject in terms of the estimated alternans amplitude from the unipolar EGMs, however, higher spatial-temporal variation was present in LQTS torso and epicardium measurements, which was consistent through three different methods of alternans estimation. We conclude that spatial-temporal analysis of EGMs in ECGI will pave the way towards enhanced usefulness in the clinical practice, so that atomic signal processing approach should be conveniently revisited to be able to deal with the great amount of information that ECGI conveys for the clinician.

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.

The Ideal Cardiac Mapping System

Cardiac mapping has witnessed significant and unprecedented progress over more than a century. At present, several mapping/imaging technologies are commercially available, alone or in combination. This article briefly discusses the advantages and limitations (disadvantages) of each technique.