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

Kalman-filter-based EEG source localization

This thesis uses the Kalman filter (KF) to solve the electroencephalographic (EEG) inverse problem to image its neuronal sources. Chapter 1 introduces EEG source localization and the KF and discusses how it can solve the inverse problem. Chapter 2 introduces an EEG inverse solution using a spatially whitened KF (SWKF) to reduce the computational burden. Likelihood maximization is used to fit spatially uniform neural model parameters to simulated and clinical EEGs. The SWKF accurately reconstructs source dynamics. Filter performance is analyzed by computing the innovations’ statistical properties and identifying spatial variations in performance that could be improved by use of spatially varying parameters. Chapter 3 investigates the SWKF via one-dimensional (1D) simulations. Motivated by Chapter 2, two model parameters are given Gaussian spatial profiles to better reflect brain dynamics. Constrained optimization ensures estimated parameters have clear biophysical interpretations. Inverse solutions are also computed using the optimal linear KF. Both filters produce accurate state estimates. Spatially varying parameters are correctly identified from datasets with transient dynamics, but estimates for driven datasets are degraded by the unmodeled drive term. Chapter 4 treats the whole-brain EEG inverse problem and applies features of the 1D simulations to the SWKF of Chapter 2. Spatially varying parameters are used to model spatial variation of the alpha rhythm. The simulated EEG here exhibits wave-like patterns and spatially varying dynamics. As in Chapter 3, optimization constrains model parameters to appropriate ranges. State estimation is again reliable for simulated and clinical EEG, although spatially varying parameters do not improve accuracy and parameter estimation is unreliable, with wave velocity underestimated. Contributing factors are identified and approaches to overcome them are discussed. Chapter 5 summarizes the main findings and outlines future work

Kalman-filter-based EEG source localization

This thesis uses the Kalman filter (KF) to solve the electroencephalographic (EEG) inverse problem to image its neuronal sources. Chapter 1 introduces EEG source localization and the KF and discusses how it can solve the inverse problem. Chapter 2 introduces an EEG inverse solution using a spatially whitened KF (SWKF) to reduce the computational burden. Likelihood maximization is used to fit spatially uniform neural model parameters to simulated and clinical EEGs. The SWKF accurately reconstructs source dynamics. Filter performance is analyzed by computing the innovations’ statistical properties and identifying spatial variations in performance that could be improved by use of spatially varying parameters. Chapter 3 investigates the SWKF via one-dimensional (1D) simulations. Motivated by Chapter 2, two model parameters are given Gaussian spatial profiles to better reflect brain dynamics. Constrained optimization ensures estimated parameters have clear biophysical interpretations. Inverse solutions are also computed using the optimal linear KF. Both filters produce accurate state estimates. Spatially varying parameters are correctly identified from datasets with transient dynamics, but estimates for driven datasets are degraded by the unmodeled drive term. Chapter 4 treats the whole-brain EEG inverse problem and applies features of the 1D simulations to the SWKF of Chapter 2. Spatially varying parameters are used to model spatial variation of the alpha rhythm. The simulated EEG here exhibits wave-like patterns and spatially varying dynamics. As in Chapter 3, optimization constrains model parameters to appropriate ranges. State estimation is again reliable for simulated and clinical EEG, although spatially varying parameters do not improve accuracy and parameter estimation is unreliable, with wave velocity underestimated. Contributing factors are identified and approaches to overcome them are discussed. Chapter 5 summarizes the main findings and outlines future work

Kalbin Elektriksel Aktivitesinin 3 Boyutlu Transmembran Potansiyel Dağılımları Cinsinden Girişimsiz Olarak Görüntülenmesi

TÜBİTAK EEEAG Proje01.04.2015Vücut yüzeyi potansiyel (VYP) ölçümlerinden kalpteki elektriksel kaynakların kestirilmesine ters elektrokardiografi (EKG) problemi denir. Bu yöntem, ölümcül de olabilecek kalp hastalıklarının teşhisinde ve tedavi planlamasında hekimlere yol gösterme potansiyeline sahiptir. Ancak, bu problem kötü konumlanmış bir problemdir ve ölçümlerdeki az miktarda gürültü bile sınırsız çözümler bulunmasına yol açmaktadır. Bunun üstesinden gelebilmek için literatürde, başta Tikhonov düzenlileştirmesi olmak üzere çeşitli düzenlileştirme yöntemleri uygulanmıştır. Ancak uygulanan her yöntem farklı durumlarda test edilmiştir; henüz hangi yöntemin en iyi yöntem olduğu konusunda fikir birliği sağlanamamıştır. Son zamanlarda, üç boyutlu miyokart dokusunda da detaylı bilgi verebildiği için, transmembran potansiyelleri (TMP) cinsinden ters EKG çözümleri popülerleşmiştir. Ancak henüz bu alanda az sayıda çalışma vardır ve özellikle farklı kalp aritmilerinde farklı yöntemlerin nasıl performans sergileyeceği bilinmemektedir. Bu projede temel amaç, bu açığı kapatmak, farklı elektriksel dağılımlar için literatürdeki belli başlı yöntemlerle ters EKG problemini çözmektir. Bu projede, kapsamlı bir çalışmayla, önerilen yöntemlerin performansları aynı test verisiyle ve aynı kriterler kullanılarak objektif bir şekilde karşılaştırılabilmiştir. Ayrıca farklı aritmiler için TMP benzetimleri ve buna bağlı VYPler elde edildiği için, yöntemlerin bu farklı aritmiler karşısında nasıl bir performans sergilediği de araştırılmıştır. Öncelikle Aliev-Panfilov yöntemiyle farklı kalp aktiviteleri için TMP benzetimleri yapılmış, ardından ileri EKG problemi çözülerek bu dağılımlardan VYP dağılımları bulunmuştur. Bu dağılımlar ters EKG çözümlerinde kullanılmıştır. Uygulanan beş değişik ters EKG çözüm yönteminden her durumda en başarılı yöntemin Bayesian MAP olduğu gözlenmiştir. TTLS, LTTLS ve LSQR yöntemlerinin de uyarım noktalarını ve dalga önünü bulmakta çok kötü performans sergilemediği görülmüştür. Bu proje kapsamında iki ayrı dalda daha literatüre katkı sağlanmıştır. Bunlardan ilki, fiber yönelimlerinin TMP dağılımlarına etkilerinin incelenmesidir. Başka bir kalpten aktarılan fiber yönelimini kullanmanın izotropik varsayım kullanmaktan daha doğru sonuçlar verdiği gözlenmiştir. İkinci katkı da, TMP dağılımları cinsinden FEM yöntemi ile ileri problem çözümünün doğrulamasıdır. Uygun ağ sıklığına ulaşıldığında sayısal çözümün analitik çözüme yakınsadığı gösterilmiştir.Inverse electrocardiography is the estimation of cardiac electrical sources from body surface potential (BSP) measurements. Inverse solutions can guide the physicians for diagnosis and treatment planning of lethal heart diseases. However, inverse problem is ill-posed and even small perturbations in the measurements yield unbounded errors in the solutions. To overcome this difficulty, many regularization approaches have been proposed in literature. However, these methods have been applied and tested under varying conditions in different studies; there is no consensus among researchers on the method with the best performance. Lately, solutions in terms of transmembrane potentials (TMP) have become popular, since they provide information about the electrical activity of the three dimensional myocardium. There are few studies in this area and it is still an open question how different methods will perform under different arrythmia conditions. The main goal in this project is to solve the inverse problem in terms of TMPs, using different approaches but under the same (and diverse) cardiac conditions. First, we obtained TMP distributions for various cardiac electrical activity assumptions using Aliev-Panfilov model. Then we solved the forward ECG problem to obtain the corresponding BSPs, which were later used in the inverse problem solutions. Among the five inverse approaches, Bayesian MAP estimation had the best performance under all conditions. TTLS, LTTLS and LSQR were also successful in finding the initial stimulation points and recovering the wavefront. We made contributions in two more areas in this project. The first one is our study of fiber orientation effects on TMP distributions. We found that even using fiber orientations from a different heart is much better than using the isotropic assumption. The second one is the analytical verification of the FEM based forward problem; with an appropriate mesh size, we showed that the numerical solution converges to the analytical solution

Doctor of Philosophy

dissertationInverse Electrocardiography (ECG) aims to noninvasively estimate the electrophysiological activity of the heart from the voltages measured at the body surface, with promising clinical applications in diagnosis and therapy. The main challenge of this emerging technique lies in its mathematical foundation: an inverse source problem governed by partial differential equations (PDEs) which is severely ill-conditioned. Essential to the success of inverse ECG are computational methods that reliably achieve accurate inverse solutions while harnessing the ever-growing complexity and realism of the bioelectric simulation. This dissertation focuses on the formulation, optimization, and solution of the inverse ECG problem based on finite element methods, consisting of two research thrusts. The first thrust explores the optimal finite element discretization specifically oriented towards the inverse ECG problem. In contrast, most existing discretization strategies are designed for forward problems and may become inappropriate for the corresponding inverse problems. Based on a Fourier analysis of how discretization relates to ill-conditioning, this work proposes refinement strategies that optimize approximation accuracy o f the inverse ECG problem while mitigating its ill-conditioning. To fulfill these strategies, two refinement techniques are developed: one uses hybrid-shaped finite elements whereas the other adapts high-order finite elements. The second research thrust involves a new methodology for inverse ECG solutions called PDE-constrained optimization, an optimization framework that flexibly allows convex objectives and various physically-based constraints. This work features three contributions: (1) fulfilling optimization in the continuous space, (2) formulating rigorous finite element solutions, and (3) fulfilling subsequent numerical optimization by a primal-dual interiorpoint method tailored to the given optimization problem's specific algebraic structure. The efficacy o f this new method is shown by its application to localization o f cardiac ischemic disease, in which the method, under realistic settings, achieves promising solutions to a previously intractable inverse ECG problem involving the bidomain heart model. In summary, this dissertation advances the computational research of inverse ECG, making it evolve toward an image-based, patient-specific modality for biomedical research

Functional Mapping of Three-Dimensional Electrical Activation in Ventricles

University of Minnesota Ph.D. dissertation. 2010. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); 139 pages.Ventricular arrhythmias account for nearly 400,000 deaths per year in the United States alone. Electrical mapping of the ventricular activation could facilitate the diagnosis and treatment of arrhythmias, e.g. guiding catheter ablation. To date, both direct mapping and non-contact mapping techniques have been routinely used in electrophysiology labs for obtaining the electrical activity on the endocardial surface. Non-invasive functional mapping methods are also developed to estimate the electrical activity on the epicardium or on both epicardium and endocardium from the body surface measurements. Though successful, the results using above methods are all limited on the surface of the heart and thus cannot directly characterize the cardiac events originating within the myocardial wall. Our group's goal is to develop a functional mapping method to estimate the three-dimensional cardiac electrical activity from either non-invasive body surface potential maps or minimally-invasive intracavitary potential maps, by solving the so-called "inverse problem". Hence the information under the surface of the heart could be revealed to better characterize the cardiac activation. In the present thesis study, the previously developed three-dimensional cardiac electrical imaging (3DCEI) approach has been further investigated. Its function is expanded for not only estimating the global activation sequence but also reconstructing the potential at any myocardial site throughout the ventricle. New algorithms under the 3DCEI scheme are also explored for more powerful mapping capability. The performance of the enhanced 3DCEI approach is rigorously evaluated in both control and diseased swine models when the clinical settings are mimicked. The promising results validate the feasibility of estimating detailed three-dimensional cardiac activation by using the 3DCEI approach, and suggest that 3DCEI has great potential of guiding the clinical management of cardiac arrhythmias in a more efficient way

Contributions To The Methodology Of Electrocardiographic Imaging (ECGI) And Application Of ECGI To Study Mechanisms Of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, And Ventricular Tachycardia In Patients

ABSTRACT OF THE DISSERTATION Contributions to the Methodology of Electrocardiographic Imaging: ECGI) and Application of ECGI to Study Mechanisms of Atrial Arrhythmia, Post Myocardial Infarction Electrophysiological Substrate, and Ventricular Tachycardia in Patients by Yong Wang Doctor of Philosophy in Biomedical Engineering Washington University in St. Louis, 2009 Professor Yoram Rudy, Chair Electrocardiographic Imaging: ECGI) is a noninvasive imaging modality for cardiac electrophysiology and arrhythmia. ECGI reconstructs epicardial potentials, electrograms and isochrones from body-surface electrocardiograms combined with heart-torso geometry from computed tomography: CT). The application of a new meshless method, the Method of Fundamental Solutions: MFS) is introduced to ECGI with the following major advantages: 1. Elimination of meshing and manual mesh optimization processes, thereby enhancing automation and speeding the ECGI procedure. 2. Elimination of mesh-induced artifacts. 3. Simpler implementation. These properties of MFS enhance the practical application of ECGI as a clinical diagnostic tool. The current ECGI mode of operation is offline with generation of epicardial potential maps delayed to data acquisition. A real time ECGI procedure is proposed, by which the epicardial potentials can be reconstructed while the body surface potential data are acquired: \u3c 1msec/frame) during a clinical procedure. This development enables real-time monitoring, diagnosis, and interactive guidance of intervention for arrhythmia therapy. ECGI is applied to map noninvasively the electrophysiological substrate in eight post-MI patients during sinus rhythm: SR). Contrast-enhanced MRI: ceMRI) is conducted to determine anatomical scar. ECGI imaged regions of electrical scar corresponded closely in location, extent, and morphology to the anatomical scars. In three patients, late diastolic potentials are imaged in the scar epicardial border zone during SR. Scar-related ventricular tachycardia: VT) in two patients are imaged, showing the VT activation sequence in relation to the abnormal electrophysiological substrate. ECGI imaging the substrate in a beat-by-beat fashion could potentially help in noninvasive risk stratification for post-MI arrhythmias and facilitate substrate-based catheter ablation of these arrhythmias. ECGI is applied to eleven consecutive patients referred for VT catheter ablation procedure. ECGI is performed either before: 8 patients) or during: 3 patients) the ablation procedure. Blinded ECGI and invasive electrophysiology: EP) study results are compared. Over a wide range of VT types and locations, ECGI results are consistent with EP data regarding localization of the arrhythmia origin: including myocardial depth) and mechanism: focal, reentrant, fascicular). ECGI also provides mechanistic electrophysiological insights, relating arrhythmia patterns to the myocardial substrate. The study shows ECGI has unique potential clinical advantages, especially for hemodynamically intolerant VT or VT that is difficult to induce. Because it provides local cardiac information, ECGI may aid in better understanding of mechanisms of ventricular arrhythmia. Further prospective trials of ECGI with clinical endpoints are warranted. Many mechanisms for the initiation and perpetuation of atrial fibrillation: AF) have been demonstrated over the last several decades. The tools to study these mechanisms in humans have limitations, the most common being invasiveness of a mapping procedure. In this paper, we present simultaneous noninvasive biatrial epicardial activation sequences of AF in humans, obtained using the Electrocardiographic Imaging: ECGI) system, and analyzed in terms of mechanisms and complexity of activation patterns. We performed ECGI in 36 patients with a diagnosis of AF. To determine ECGI atrial accuracy, atrial pacing from different sites was performed in six patients: 37 pacing events), and ECGI was compared to registered CARTO images. Then, ECGI was performed on all 36 patients during AF and ECGI epicardial maps were analyzed for mechanisms and complexity. ECGI noninvasively imaged the low-amplitude signals of AF in a wide range of patients: 97% procedural success). The spatial accuracy in determining initiation sites as simulated by atrial pacing was ~ 6mm. ECGI imaged many activation patterns of AF, most commonly multiple wavelets: 92%), with pulmonary vein: 69%) and non-pulmonary vein: 62%) trigger sites. Rotor activity was seen rarely: 15%). AF complexity increased with longer clinical history of AF, though the degree of complexity of nonparoxysmal AF varied and overlapped. ECGI offers a way to identify unique epicardial activation patterns of AF in a patient-specific manner. The results are consistent with contemporary animal models of AF mechanisms and highlight the coexistence of a variety of mechanisms among patients

Bioelectrical strategies for image-guided therapies

Thesis (Ph. D.)--Harvard-MIT Division of Health Sciences and Technology, 2007.Includes bibliographical references (leaves 152-157).There is a pressing need in minimally-invasive surgery for novel imaging methods that can rapidly and accurately localize the surgical instrument and its target. We have developed two novel localization methods for the guidance of cardiac ablation and other minimally-invasive therapies. The first method, the Inverse Solution Guidance Algorithm (ISGA), is for the non-invasive and rapid localization of the site of origin of an arrhythmia and an ablation catheter tip from body-surface ECG signals. We have substantially developed ISGA to provide accurate catheter guidance even in the presence of significant electrical inhomogeneities, and we have evaluated the method in numerical simulations and phantom studies. Due to the rapidity of arrhythmic origin localization, ISGA may prove a highly effective means of guiding the ablative therapy of hemodynamically-unstable VT. The second method, the Bioelectrical Image Guidance (BIG) Method, is a novel algorithm for the accurate and inexpensive guidance of a wide-range of minimally-invasive surgeries, from cardiac ablation to breast cancer biopsy.(cont.) The surgical instrument is localized within a detailed 3-D MRI or CT image by applying currents to the body surface and comparing the potentials measured at the instrument tip with potential distributions simulated prior to the surgery. We have developed and evaluated this method in numerical simulations. We have also built an experimental guidance system and tested it in a phantom model. Our results indicate that the BIG Method may one day provide an accurate and convenient means by which to guide minimally-invasive surgery within a highly detailed anatomical image.by Maya E. Barley.Ph.D

Aerospace medicine and biology: A cumulative index to the continuing bibliography of the 1973 issues

A cumulative index to the abstracts contained in Supplements 112 through 123 of Aerospace Medicine and Biology A Continuing Bibliography is presented. It includes three indexes: subject, personal author, and corporate source