Noninvasive Electrocardiographic Imaging (ECGi) to Guide Catheter Ablation of Scar-related Ventricular Tachycardia

Scar-related VT is caused by local \textit{short circuits} of electrical propagation formed by slow-conducting channels of surviving tissue within a scar. Catheter ablation treats scar-related VT by destroying the critical channel of surviving tissues. Its efficacy heavily relies on how well the channels critical to the formation of VT circuits can be localized. Unfortunately, in current practice, this relies on invasive catheter mapping that falls short in several critical aspects: up to 90$\%$ of the VT circuits are too short-lived to be mapped, the mapping cannot be done non-invasively prior to the ablation procedure, and the mapping is restricted to one heart surface at a time. Electrocardiographic imaging (ECGi) is a noninvasive approach that reconstructs cardiac electrical signals from a very dense body surface electrocardiogram (ECG) in combination with patient-specific geometries of the heart and torso. In this dissertation, we investigate the clinical utility of ECGi in guiding catheter ablation of scar-related VT. Specifically, we investigate two open questions that are not well-understood in the potential of ECGi for mapping VT circuits. First, instead of commonly-used epicardial ECGi, we investigate the validity of simultaneous epicardial and endocardial ECGi mapping of VT circuits, and the possibility of using information from these two surfaces to infer the morphology of 3D circuits. Second, we investigate the integration of ECGi electrical information of VT circuits with magnetic resonance imaging (MRI) of scar analysis for joint electrical and structural delineation of the substrates for VT circuits. These studies were performed on a combination of computer simulation, animal model, and human subject data. Experimental results showed that epi-endo ECGi mapping could reconstruct VT circuits, differentiate 2D versus 3D circuits, and provide information about the location of the VT circuit beneath the surface. They also showed that integrated MRI-ECGi analysis offered a quantitative characterization of the scar substrate that forms a VT circuit. These outcomes showed that simultaneous epi-endo ECGi in the combination of MRI structural scar imaging may provide a viable augmentation to the current practice of invasive catheter mapping. It may help clinicians plan for the ablation prior to the procedure by equipping them with knowledge about a VT circuit\u27s critical components, the surfaces that are involved, and the 3D morphology of the VT circuit

ECG Imaging of Ventricular Activity in Clinical Applications

ECG imaging was performed in humans to reconstruct ventricular activation patterns and localize their excitation origins. The precision of the non-invasive reconstructions was evaluated against invasive measurements and found to be in line with the state-of-the-art literature. Statistics were produced for various excitation origins and reveal the beat-to-beat robustness of the imaging method

Non-Invasive Electrocardiographic Imaging of Ventricular Activities: Data-Driven and Model-Based Approaches

Die vorliegende Arbeit beleuchtet ausgewählte Aspekte der Vorwärtsmodellierung, so zum Beispiel die Simulation von Elektro- und Magnetokardiogrammen im Falle einer elektrisch stillen Ischämie sowie die Anpassung der elektrischen Potentiale unter Variation der Leitfähigkeiten. Besonderer Fokus liegt auf der Entwicklung neuer Regularisierungsalgorithmen sowie der Anwendung und Bewertung aktuell verwendeter Methoden in realistischen in silico bzw. klinischen Studien

Personalized noninvasive imaging of volumetric cardiac electrophysiology

Three-dimensionally distributed electrical functioning is the trigger of mechanical contraction of the heart. Disturbance of this electrical flow is known to predispose to mechanical catastrophe but, due to its amenability to certain intervention techniques, a detailed understanding of subject-specific cardiac electrophysiological conditions is of great medical interest. In current clinical practice, body surface potential recording is the standard tool for diagnosing cardiac electrical dysfunctions. However, successful treatments normally require invasive catheter mapping for a more detailed observation of these dysfunctions. In this dissertation, we take a system approach to pursue personalized noninvasive imaging of volumetric cardiac electrophysiology. Under the guidance of existing scientific knowledge of the cardiac electrophysiological system, we extract the subject specific cardiac electrical information from noninvasive body surface potential mapping and tomographic imaging data of individual subjects. In this way, a priori knowledge of system physiology leads the physiologically meaningful interpretation of personal data; at the same time, subject-specific information contained in the data identifies parameters in individual systems that differ from prior knowledge. Based on this perspective, we develop a physiological model-constrained statistical framework for the quantitative reconstruction of the electrical dynamics and inherent electrophysiological property of each individual cardiac system. To accomplish this, we first develop a coupled meshfree-BE (boundary element) modeling approach to represent existing physiological knowledge of the cardiac electrophysiological system on personalized heart-torso structures. Through a state space system approach and sequential data assimilation techniques, we then develop statistical model-data coupling algorithms for quantitative reconstruction of volumetric transmembrane potential dynamics and tissue property of 3D myocardium from body surface potential recoding of individual subjects. We also introduce a data integration component to build personalized cardiac electrophysiology by fusing tomographic image and BSP sequence of the same subject. In addition, we develop a computational reduction strategy that improves the efficiency and stability of the framework. Phantom experiments and real-data human studies are performed for validating each of the framework’s major components. These experiments demonstrate the potential of our framework in providing quantitative understanding of volumetric cardiac electrophysiology for individual subjects and in identifying latent threats in individual’s heart. This may aid in personalized diagnose, treatment planning, and fundamentally, prevention of fatal cardiac arrhythmia

Innovations and mechanisms in pacing therapy for heart failure

Despite pharmacological advances, heart failure remains a major cause of mortality and morbidity. Pacing therapy for heart failure was achieved in the 1990s with the advent of biventricular pacing (BVP). BVP shortens ventricular activation time and has thus been referred to as ‘cardiac resynchronization therapy’ (CRT). However BVP has other effects including shortening of atrioventricular delay: the contributions of its effects to its overall benefit have yet to be elucidated. Ventricular activation is not normalised by BVP, indicating scope for more effective resynchronization. This thesis explores mechanisms and innovations in pacing therapy for heart failure through measurement of haemodynamic and electrical parameters with high precision and resolution during BVP, right ventricular pacing (RVP) and His bundle pacing (HBP), where the His-Purkinje conduction system is directly stimulated. HBP offers both an innovation in pacing and a model to study conventional pacing. HBP can deliver physiological CRT by overcoming left bundle branch block (LBBB) to normalise QRS appearances but its performance relative to BVP is not known. When performed proximally, or using lower energy, HBP can preserve intrinsic LBBB. In Chapter 3, the electro-mechanical effects of conventional BVP are compared with LBBB correction by HBP. Chapter 4 uses non-invasive electrical mapping to identify mechanisms and predictors of LBBB correction by HBP, comparing it with narrow QRS. Capture of the His bundle can be alone (selective HBP) or alongside myocardial capture (non-selective): the effect of this on HBP is studied in Chapter 5. In Chapter 6, the haemodynamic effects of proximal/low-energy HBP, where LBBB is preserved but atrioventricular timing can be optimised, is compared to BVP and RVP to measure the contribution of atrioventricular delay shortening to the overall benefit of BVP. By evaluating innovative therapies and improving our understanding of existing therapies, hopefully this thesis will advance pacing therapy for heart failure.Open Acces

Uncertainty Quantification and Reduction in Cardiac Electrophysiological Imaging

Cardiac electrophysiological (EP) imaging involves solving an inverse problem that infers cardiac electrical activity from body-surface electrocardiography data on a physical domain defined by the body torso. To avoid unreasonable solutions that may fit the data, this inference is often guided by data-independent prior assumptions about different properties of cardiac electrical sources as well as the physical domain. However, these prior assumptions may involve errors and uncertainties that could affect the inference accuracy. For example, common prior assumptions on the source properties, such as fixed spatial and/or temporal smoothness or sparseness assumptions, may not necessarily match the true source property at different conditions, leading to uncertainties in the inference. Furthermore, prior assumptions on the physical domain, such as the anatomy and tissue conductivity of different organs in the thorax model, represent an approximation of the physical domain, introducing errors to the inference. To determine the robustness of the EP imaging systems for future clinical practice, it is important to identify these errors/uncertainties and assess their impact on the solution. This dissertation focuses on the quantification and reduction of the impact of uncertainties caused by prior assumptions/models on cardiac source properties as well as anatomical modeling uncertainties on the EP imaging solution. To assess the effect of fixed prior assumptions/models about cardiac source properties on the solution of EP imaging, we propose a novel yet simple Lp-norm regularization method for volumetric cardiac EP imaging. This study reports the necessity of an adaptive prior model (rather than fixed model) for constraining the complex spatiotemporally changing properties of the cardiac sources. We then propose a multiple-model Bayesian approach to cardiac EP imaging that employs a continuous combination of prior models, each re-effecting a specific spatial property for volumetric sources. The 3D source estimation is then obtained as a weighted combination of solutions across all models. Including a continuous combination of prior models, our proposed method reduces the chance of mismatch between prior models and true source properties, which in turn enhances the robustness of the EP imaging solution. To quantify the impact of anatomical modeling uncertainties on the EP imaging solution, we propose a systematic statistical framework. Founded based on statistical shape modeling and unscented transform, our method quantifies anatomical modeling uncertainties and establish their relation to the EP imaging solution. Applied on anatomical models generated from different image resolutions and different segmentations, it reports the robustness of EP imaging solution to these anatomical shape-detail variations. We then propose a simplified anatomical model for the heart that only incorporates certain subject-specific anatomical parameters, while discarding local shape details. Exploiting less resources and processing for successful EP imaging, this simplified model provides a simple clinically-compatible anatomical modeling experience for EP imaging systems. Different components of our proposed methods are validated through a comprehensive set of synthetic and real-data experiments, including various typical pathological conditions and/or diagnostic procedures, such as myocardial infarction and pacing. Overall, the methods presented in this dissertation for the quantification and reduction of uncertainties in cardiac EP imaging enhance the robustness of EP imaging, helping to close the gap between EP imaging in research and its clinical application

Bayesian Inference with Combined Dynamic and Sparsity Models: Application in 3D Electrophysiological Imaging

Data-driven inference is widely encountered in various scientific domains to convert the observed measurements into information that cannot be directly observed about a system. Despite the quickly-developing sensor and imaging technologies, in many domains, data collection remains an expensive endeavor due to financial and physical constraints. To overcome the limits in data and to reduce the demand on expensive data collection, it is important to incorporate prior information in order to place the data-driven inference in a domain-relevant context and to improve its accuracy. Two sources of assumptions have been used successfully in many inverse problem applications. One is the temporal dynamics of the system (dynamic structure). The other is the low-dimensional structure of a system (sparsity structure). In existing work, these two structures have often been explored separately, while in most high-dimensional dynamic system they are commonly co-existing and contain complementary information. In this work, our main focus is to build a robustness inference framework to combine dynamic and sparsity constraints. The driving application in this work is a biomedical inverse problem of electrophysiological (EP) imaging, which noninvasively and quantitatively reconstruct transmural action potentials from body-surface voltage data with the goal to improve cardiac disease prevention, diagnosis, and treatment. The general framework can be extended to a variety of applications that deal with the inference of high-dimensional dynamic systems

A novel technique for guiding ablative therapy of cardiac arrhythmias

Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1999.Includes bibliographical references (leaves 173-180).by Antonis A. Armoundas.Ph.D

The action potential duration and repolarization of the human ventricle and its relation to the body surface ECG

Repolarization dispersion has been associated with increased risk of cardiac arrhythmia in animal studies, but whether it is present in humans and how its manifests on the surface ECG is unknown. This thesis aimed to bridge the gap between our basic cellular and intracardiac understanding of cardiac electrical activity with the surface ECG focusing specifically on cardiac repolarization and the surface ECG. Restitution studies in humans with structurally normal and abnormal hearts were performed, looking at transmural and regional gradients of repolarization. The data were then analysed to assess the association of intracardiac repolarization with the surface T-wave. Finally a novel non-invasive ECG (ECGI) was used to study the electrical substrate in patients with arrhythmogenic right ventricular cardiomyopathy (ARVC). In normal human heart restitution studies epicardial APD was shorter than endocardial APD, with smaller differences in the apicobasal orientation. It was found that, local repolarization times in the right and left ventricle occur along the upslope of the body surface T-wave in leads V1-V3 and V5,V6 and Lead-I respectively; and the difference between the end of the upslope in V1 and V6 provides a good representation of right to left dispersion of repolarization. In patients with structurally abnormal hearts myocardial scar, regardless of pathology, resulted in prolonged APD compared to normal healthy tissue diminishing transmural gradients of APD and resulting in localised transmural dispersion of repolarization. Finally ECGI characterised the electrophysiological substrate in ARVC, demonstrating prolonged APD in these patients compared to control patients. ECGI was able to demonstrate the presence of electrophysiological changes before changes were visible on cardiac MRI. In conclusion, this work shows that APD gradients are present in the intact human heart, and can be demonstrated both using contact electrophysiological mapping and on methods that incorporate the body surface ECG