A Cardiac Shape Model for Segmentation Uncertainty Quantification.

Segmentation of cardiac images is a variable component of many patient specific computational pipelines, yet its impact on simulated results are still not fully understood. A hurdle to to exploring the impact of the segmentation variability is the technical challenge of building a statistical shape model of the ventricles. In this study, we improved open our previous shape analysis by creating a unified shape model including both the epicardium and endocardium.

Uncertainty Quantification of the Effects of Segmentation Variability in ECGI.

Despite advances in many of the techniques used in Electrocardiographic Imaging (ECGI), uncertainty remains insufficiently quantified for many aspects of the pipeline. The effect of geometric uncertainty, particularly due to segmentation variability, may be the least explored to date. We use statistical shape modeling and uncertainty quantification (UQ) to compute the effect of segmentation variability on ECGI solutions.

The Role of Myocardial Fiber Direction in Epicardial Activation Patterns via Uncertainty Quantification.

Fiber structure governs the spread of excitation in the heart; however, little is known about the effects of physiological variability in fiber orientation on epicardial activation. To investigate these effects, we implemented ventricular simulations of activation using rule-based fiber orientations, and robust uncertainty quantification algorithms to capture detailed maps of model sensitivity. Specifically, we implemented polynomial chaos expansion, which allows for robust exploration with reduced computational demand through an emulator function to approximate the underlying forward model.

The electrocardiographic forward problem: A benchmark study.

Background: Electrocardiographic forward problems are crucial components for noninvasive electrocardiographic imaging (ECGI) that compute torso potentials from cardiac source measurements. Forward problems have few sources of error as they are physically well posed and supported by mature numerical and computational techniques. However, the residual errors reported from experimental validation studies between forward computed and measured torso signals remain surprisingly high.

Using UncertainSCI to Quantify Uncertainty in Cardiac Simulations.

Cardiac simulations have become increasingly accurate at representing physiological processes. However, simulations often fail to capture the impact of parameter uncertainty in predictions. Uncertainty quantification (UQ) is a set of techniques that captures variability in simulation output based on model assumptions.

Experimental Validation of Image-Based Modeling of Torso Surface Potentials During Acute Myocardial Ischemia.

Introduction: Myocardial ischemia is an early clinical indicator of several underlying cardiac pathologies, including coronary artery disease, Takatsobu cardiomyopathy, and coronary artery dissection. Significant progress has been made in computing body-surface potentials from cardiac sources by solving the forward problem of electrocardiography. However, the lack of in vivo studies to validate such computations from ischemic sources has limited the translational potential of such models.

Novel experimental model for studying the spatiotemporal electrical signature of acute myocardial ischemia: a translational platform.

Unlabelled: Myocardial ischemia is one of the most common cardiovascular pathologies and can indicate many severe and life threatening diseases. Despite these risks, current electrocardiographic detection techniques for ischemia are mediocre at best, with reported sensitivity and specificity ranging from 50%-70% and 70%-90%, respectively.

Objective: To improve this performance, we set out to develop an experimental preparation to induce, detect, and analyze bioelectric sources of myocardial ischemia and determine how these sources reflect changes in body-surface potential measurements.

Correcting Undersampled Cardiac Sources in Equivalent Double Layer Forward Simulations.

Electrocardiographic Imaging (ECGI) requires robust ECG forward simulations to accurately calculate cardiac activity. However, many questions remain regarding ECG forward simulations, for instance: there are not common guidelines for the required cardiac source sampling. In this study we test equivalent double layer (EDL) forward simulations with differing cardiac source resolutions and different spatial interpolation techniques.

GRÖMeR: A Pipeline for Geodesic Refinement of Mesh Registration.

The electrical signals produced by the heart can be used to assess cardiac health and diagnose adverse pathologies. Experiments on large mammals provide essential sources of these signals through measurements of up to 1000 simultaneous, distributed locations throughout the heart and torso. To perform accurate spatial analysis of the resulting electrical recordings, researchers must register the locations of each electrode, typically by defining correspondence points from post-experiment, three-dimensional imaging, and directly measured surface electrodes.

Validating defibrillation simulation in a human-shaped phantom.

Background: We previously developed a computational model to aid clinicians in positioning implantable cardioverter-defibrillators (ICDs), especially in the case of abnormal anatomies that commonly arise in pediatric cases. We have validated the model clinically on the body surface; however, validation within the volume of the heart is required to establish complete confidence in the model and improve its use in clinical settings.

Objective: The goal of this study was to use an animal model and thoracic phantom to record the ICD potential field within the heart and on the torso to validate our defibrillation simulation system.

Effect of Segmentation Variation on ECG Imaging.

ECG imaging (ECGI) is the process of calculating electrical cardiac activity from body surface recordings from the geometry and conductivity of the torso volume. A key first step to create geometric models for ECGI and a possible source of considerable variability is to segment the surface of the heart. We hypothesize that this variation in cardiac segmentation will produce variation in the computed ventricular surface potentials from ECGI.

Electrocardiographic Comparison of Dobutamine and BRUCE Cardiac Stress Testing With High Resolution Mapping in Experimental Models.

Clinical tests to detect acute myocardial ischemia induce transient cardiac stress by means of exercise or pharmaceutical stimulation and measure electrical changes of the heart on the body surface via an electrocardiogram (ECG). Such tests assume that both stress mechanisms induce identical-or at least similar-forms of ischemia. To improve electrocardiographic detection of myocardial ischemia, we must study how varied stressing agents (pharmacological or paced stressors) change electrocardiographic signatures.

Optimizing the Reconstruction of Cardiac Potentials Using a Novel High Resolution Pericardiac Cage.

Introduction: Experimental preparations in which cardiac and torso recordings are made simultaneously typically do not have uniform sampling around the entire surface of the heart. To fill in the resulting gaps in coverage, signals captured from the sampled region are extended to the unsampled region of the heart before being utilized in computational models. The resulting errors have never been evaluated systematically.

Overcoming Barriers to Quantification and Comparison of Electrocardiographic Imaging Methods: A Community-Based Approach.

There has been a recent upsurge in the development of electrocardiographic imaging (ECGI) methods, along with a significant increase in clinical application. To better assess the state-of-the-art, enable reliable progress, and facilitate clinical adoption, it is important to be able to compare results in a comprehensive manner, scientifically and clinically. However, studies vary in modeling choices, computational methods, validation mechanisms and metrics, and clinical applications, making unified evaluation and comparison of ECGI a critical challenge.

A Framework for Image-Based Modeling of Acute Myocardial Ischemia Using Intramurally Recorded Extracellular Potentials.

The biophysical basis for electrocardiographic evaluation of myocardial ischemia stems from the notion that ischemic tissues develop, with relative uniformity, along the endocardial aspects of the heart. These injured regions of subendocardial tissue give rise to intramural currents that lead to ST segment deflections within electrocardiogram (ECG) recordings. The concept of subendocardial ischemic regions is often used in clinical practice, providing a simple and intuitive description of ischemic injury; however, such a model grossly oversimplifies the presentation of ischemic disease-inadvertently leading to errors in ECG-based diagnoses.

The Consortium for Electrocardiographic Imaging.

Electrocardiographic imaging (ECGI) has recently gained attention as a viable diagnostic tool for reconstructing cardiac electrical activity in normal hearts as well as in cardiac arrhythmias. However, progress has been limited by the lack of both standards and unbiased comparisons of approaches and techniques across the community, as well as the consequent difficulty of effective collaboration across research groups..

The Role of Reduced Left Ventricular, Systolic Blood Volumes in ST Segment Potentials Overlying Diseased Tissue of the Ischemic Heart.

Myocardial ischemia is the response of the heart to reduced coronary blood flow, leading to changes in ST segment potentials. ST segment depression is regarded as an indicator of nontransmural myocardial ischemia; however, not all nontransmural ischemia results in ST depression. This apparent discrepancy may be the result of many complex factors in cardiac response mechanisms to reduced blood flow.

New Additions to the Toolkit for Forward/Inverse Problems in Electrocardiography within the SCIRun Problem Solving Environment.

Cardiac electrical imaging often requires the examination of different forward and inverse problem formulations based on mathematical and numerical approximations of the underlying source and the intervening volume conductor that can generate the associated voltages on the surface of the body. If the goal is to recover the source on the heart from body surface potentials, the solution strategy must include numerical techniques that can incorporate appropriate constraints and recover useful solutions, even though the problem is badly posed. Creating complete software solutions to such problems is a daunting undertaking.

A toolkit for forward/inverse problems in electrocardiography within the SCIRun problem solving environment.

Computational modeling in electrocardiography often requires the examination of cardiac forward and inverse problems in order to non-invasively analyze physiological events that are otherwise inaccessible or unethical to explore. The study of these models can be performed in the open-source SCIRun problem solving environment developed at the Center for Integrative Biomedical Computing (CIBC). A new toolkit within SCIRun provides researchers with essential frameworks for constructing and manipulating electrocardiographic forward and inverse models in a highly efficient and interactive way.

Measurement of Defibrillator Surface Potentials for Simulation Verification.

Despite the growing use of implantable cardioverter defibrillators (ICDs) in adults and children, there has been little progress in optimizing device and electrode placement. To facilitate effective placement of ICDs, especially in pediatric cases, we have developed a predictive model that evaluates the efficacy of a delivered shock. Most recently, we have also developed an experimental validation approach based on measurements from clinical cases.