Influence of Material Parameter Variability on the Predicted Coronary Artery Biomechanical Environment via Uncertainty Quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Influence of material parameter variability on the predicted coronary artery biomechanical environment via uncertainty quantification.

Central to the clinical adoption of patient-specific modeling strategies is demonstrating that simulation results are reliable and safe. Indeed, simulation frameworks must be robust to uncertainty in model input(s), and levels of confidence should accompany results. In this study, we applied a coupled uncertainty quantification-finite element (FE) framework to understand the impact of uncertainty in vascular material properties on variability in predicted stresses. Univariate probability distributions were fit to material parameters derived from layer-specific mechanical behavior testing of human coronary tissue. Parameters were assumed to be probabilistically independent, allowing for efficient parameter ensemble sampling. In an idealized coronary artery geometry, a forward FE model for each parameter ensemble was created to predict tissue stresses under physiologic loading. An emulator was constructed within the UncertainSCI software using polynomial chaos techniques, and statistics and sensitivities were directly computed. Results demonstrated that material parameter uncertainty propagates to variability in predicted stresses across the vessel wall, with the largest dispersions in stress within the adventitial layer. Variability in stress was most sensitive to uncertainties in the anisotropic component of the strain energy function. Moreover, unary and binary interactions within the adventitial layer were the main contributors to stress variance, and the leading factor in stress variability was uncertainty in the stress-like material parameter that describes the contribution of the embedded fibers to the overall artery stiffness. Results from a patient-specific coronary model confirmed many of these findings. Collectively, these data highlight the impact of material property variation on uncertainty in predicted artery stresses and present a pipeline to explore and characterize forward model uncertainty in computational biomechanics.

Uncertainty quantification of the effect of cardiac position variability in the inverse problem of electrocardiographic imaging.

Objective.Electrocardiographic imaging (ECGI) is a functional imaging modality that consists of two related problems, the forward problem of reconstructing body surface electrical signals given cardiac bioelectric activity, and the inverse problem of reconstructing cardiac bioelectric activity given measured body surface signals. ECGI relies on a model for how the heart generates bioelectric signals which is subject to variability in inputs. The study of how uncertainty in model inputs affects the model output is known as uncertainty quantification (UQ). This study establishes develops, and characterizes the application of UQ to ECGI.Approach.We establish two formulations for applying UQ to ECGI: a polynomial chaos expansion (PCE) based parametric UQ formulation (PCE-UQ formulation), and a novel UQ-aware inverse formulation which leverages our previously established 'joint-inverse' formulation (UQ joint-inverse formulation). We apply these to evaluate the effect of uncertainty in the heart position on the ECGI solutions across a range of ECGI datasets.Main results.We demonstrated the ability of our UQ-ECGI formulations to characterize the effect of parameter uncertainty on the ECGI inverse problem. We found that while the PCE-UQ inverse solution provided more complex outputs such as sensitivities and standard deviation, the UQ joint-inverse solution provided a more interpretable output in the form of a single ECGI solution. We find that between these two methods we are able to assess a wide range of effects that heart position variability has on the ECGI solution.Significance.This study, for the first time, characterizes in detail the application of UQ to the ECGI inverse problem. We demonstrated how UQ can provide insight into the behavior of ECGI using variability in cardiac position as a test case. This study lays the groundwork for future development of UQ-ECGI studies, as well as future development of ECGI formulations which are robust to input parameter variability.

Tipping the scales of understanding: An engineering approach to design and implement whole-body cardiac electrophysiology experimental models.

The study of cardiac electrophysiology is built on experimental models that span all scales, from ion channels to whole-body preparations. Novel discoveries made at each scale have contributed to our fundamental understanding of human cardiac electrophysiology, which informs clinicians as they detect, diagnose, and treat complex cardiac pathologies. This expert review describes an engineering approach to developing experimental models that is applicable across scales. The review also outlines how we applied the approach to create a set of multiscale whole-body experimental models of cardiac electrophysiology, models that are driving new insights into the response of the myocardium to acute ischemia. Specifically, we propose that researchers must address three critical requirements to develop an effective experimental model: 1) how the experimental model replicates and maintains human physiological conditions, 2) how the interventions possible with the experimental model capture human pathophysiology, and 3) what signals need to be measured, at which levels of resolution and fidelity, and what are the resulting requirements of the measurement system and the access to the organs of interest. We will discuss these requirements in the context of two examples of whole-body experimental models, a closed chest in situ model of cardiac ischemia and an isolated-heart, torso-tank preparation, both of which we have developed over decades and used to gather valuable insights from hundreds of experiments.

All Roads Lead to Rome: Diverse Etiologies of Tricuspid Regurgitation Create a Predictable Constellation of Right Ventricular Shape Changes.

Introduction: Myriad disorders cause right ventricular (RV) dilation and lead to tricuspid regurgitation (TR). Because the thin-walled, flexible RV is mechanically coupled to the pulmonary circulation and the left ventricular septum, it distorts with any disturbance in the cardiopulmonary system. TR, therefore, can result from pulmonary hypertension, left heart failure, or intrinsic RV dysfunction; but once it occurs, TR initiates a cycle of worsening RV volume overload, potentially progressing to right heart failure. Characteristic three-dimensional RV shape-changes from this process, and changes particular to individual TR causes, have not been defined in detail. Methods: Cardiac MRI was obtained in 6 healthy volunteers, 41 patients with ≥ moderate TR, and 31 control patients with cardiac disease without TR. The mean shape of each group was constructed using a three-dimensional statistical shape model via the particle-based shape modeling approach. Changes in shape were examined across pulmonary hypertension and congestive heart failure subgroups using principal component analysis (PCA). A logistic regression approach based on these PCA modes identified patients with TR using RV shape alone. Results: Mean RV shape in patients with TR exhibited free wall bulging, narrowing of the base, and blunting of the RV apex compared to controls (p < 0.05). Using four primary PCA modes, a logistic regression algorithm identified patients with TR correctly with 82% recall and 87% precision. In patients with pulmonary hypertension without TR, RV shape was narrower and more streamlined than in healthy volunteers. However, in RVs with TR and pulmonary hypertension, overall RV shape continued to demonstrate the free wall bulging characteristic of TR. In the subgroup of patients with congestive heart failure without TR, this intermediate state of RV muscular hypertrophy was not present. Conclusion: The multiple causes of TR examined in this study changed RV shape in similar ways. Logistic regression classification based on these shape changes reliably identified patients with TR regardless of etiology. Furthermore, pulmonary hypertension without TR had unique shape features, described here as the "well compensated" RV. These results suggest shape modeling as a promising tool for defining severity of RV disease and risk of decompensation, particularly in patients with pulmonary hypertension.

Reconstruction of cardiac position using body surface potentials.

Electrocardiographic imaging (ECGI) is a noninvasive technique to assess the bioelectric activity of the heart which has been applied to aid in clinical diagnosis and management of cardiac dysfunction. ECGI is built on mathematical models that take into account several patient specific factors including the position of the heart within the torso. Errors in the localization of the heart within the torso, as might arise due to natural changes in heart position from respiration or changes in body position, contribute to errors in ECGI reconstructions of the cardiac activity, thereby reducing the clinical utility of ECGI. In this study we present a novel method for the reconstruction of cardiac geometry utilizing noninvasively acquired body surface potential measurements. Our geometric correction method simultaneously estimates the cardiac position over a series of heartbeats by leveraging an iterative approach which alternates between estimating the cardiac bioelectric source across all heartbeats and then estimating cardiac positions for each heartbeat. We demonstrate that our geometric correction method is able to reduce geometric error and improve ECGI accuracy in a wide range of testing scenarios. We examine the performance of our geometric correction method using different activation sequences, ranges of cardiac motion, and body surface electrode configurations. We find that after geometric correction resulting ECGI solution accuracy is improved and variability of the ECGI solutions between heartbeats is substantially reduced.

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.

Simultaneous Multi-Heartbeat ECGI Solution with a Time-Varying Forward Model: a Joint Inverse Formulation.

Electrocardiographic imaging (ECGI) is an effective tool for noninvasive diagnosis of a range of cardiac dysfunctions. ECGI leverages a model of how cardiac bioelectric sources appear on the torso surface (the forward problem) and uses recorded body surface potential signals to reconstruct the bioelectric source (the inverse problem). Solutions to the inverse problem are sensitive to noise and variations in the body surface potential (BSP) recordings such as those caused by changes or errors in cardiac position. Techniques such as signal averaging seek to improve ECGI solutions by incorporating BSP signals from multiple heartbeats into an averaged BSP with a higher SNR to use when estimating the cardiac bioelectric source. However, signal averaging is limited when it comes to addressing sources of BSP variability such as beat to beat differences in the forward solution. We present a novel joint inverse formulation to solve for the cardiac source given multiple BSP recordings and known changes in the forward solution, here changes in the heart position. We report improved ECGI accuracy over signal averaging and averaged individual inverse solutions using this joint inverse formulation across multiple activation sequence types and regularization techniques with measured canine data and simulated heart motion. Our joint inverse formulation builds upon established techniques and consequently can easily be applied with many existing regularization techniques, source models, and forward problem formulations.

Pharmacological and simulated exercise cardiac stress tests produce different ischemic signatures in high-resolution experimental mapping studies.

OBJECTIVE: Test the hypothesis that exercise and pharmacological cardiac stressors create different electrical ischemic signatures. INTRODUCTION: Current clinical stress tests for detecting ischemia lack sensitivity and specificity. One unexplored source of the poor detection is whether pharmacological stimulation and regulated exercise produce identical cardiac stress. METHODS: We used a porcine model of acute myocardial ischemia in which animals were instrumented with transmural plunge-needle electrodes, an epicardial sock array, and torso arrays to simultaneously measure cardiac electrical signals within the heart wall, the epicardial surface, and the torso surface, respectively. Ischemic stress via simulated exercise and pharmacological stimulation were created with rapid electrical pacing and dobutamine infusion, respectively, and mimicked clinical stress tests of five 3-minute stages. Perfusion to the myocardium was regulated by a hydraulic occluder around the left anterior descending coronary artery. Ischemia was measured as deflections to the ST-segment on ECGs and electrograms. RESULTS: Across eight experiments with 30 (14 simulated exercise and 16 dobutamine) ischemic interventions, the spatial correlations between exercise and pharmacological stress diverged at stage three or four during interventions (p<0.05). We found more detectable ST-segment changes on the epicardial surface during simulated exercise than with dobutamine (p<0.05). The intramyocardial ischemia formed during simulated exercise had larger ST40 potential gradient magnitudes (p<0.05). CONCLUSION: We found significant differences on the epicardium between cardiac stress types using our experimental model, which became more pronounced at the end stages of each test. A possible mechanism for these differences was the larger ST40 potential gradient magnitudes within the myocardium during exercise. The presence of microvascular dysfunction during exercise and its absence during dobutamine stress may explain these differences.

Transient recovery of epicardial and torso ST-segment ischemic signals during cardiac stress tests: A possible physiological mechanism.

BACKGROUND: Acute myocardial ischemia has several characteristic ECG findings, including clinically detectable ST-segment deviations. However, the sensitivity and specificity of diagnosis based on ST-segment changes are low. Furthermore, ST-segment deviations have been shown to be transient and spontaneously recover without any indication the ischemic event has subsided. OBJECTIVE: Assess the transient recovery of ST-segment deviations on remote recording electrodes during a partial occlusion cardiac stress test and compare them to intramyocardial ST-segment deviations. METHODS: We used a previously validated porcine experimental model of acute myocardial ischemia with controllable ischemic load and simultaneous electrical measurements within the heart wall, on the epicardial surface, and on the torso surface. Simulated cardiac stress tests were induced by occluding a coronary artery while simultaneously pacing rapidly or infusing dobutamine to stimulate cardiac function. Postexperimental imaging created anatomical models for data visualization and quantification. Markers of ischemia were identified as deviations in the potentials measured at 40% of the ST-segment. Intramural cardiac conduction speed was also determined using the inverse gradient method. We assessed changes in intramyocardial ischemic volume proportion, conduction speed, clinical presence of ischemia on remote recording arrays, and regional changes to intramyocardial ischemia. We defined the peak deviation response time as the time interval after onset of ischemia at which maximum ST-segment deviation was achieved, and ST-recovery time was the interval when ST deviation returned to below thresholded of ST elevation. RESULTS: In both epicardial and torso recordings, the peak ST-segment deviation response time was 4.9±1.1 min and the ST-recovery time was approximately 7.9±2.5 min, both well before the termination of the ischemic stress. At peak response time, conduction speed was reduced by 50% and returned to near baseline at ST-recovery. The overall ischemic volume proportion initially increased, on average, to 37% at peak response time; however, it recovered to only 30% at the ST-recovery time. By contrast, the subepicardial region of the myocardial wall showed 40% ischemic volume at peak response time and recovered much more strongly to 25% as epicardial ST-segment deviations returned to baseline. CONCLUSION: Our data show that remote ischemic signal recovery correlates with a recovery of the subepicardial myocardium, whereas subendocardial ischemic development persists.

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. OBJECTIVE: To test the hypothesis that incomplete cardiac source sampling, especially above the atrioventricular (AV) plane is a major contributor to forward solution errors. METHODS: We used a modified Langendorff preparation suspended in a human-shaped electrolytic torso-tank and a novel pericardiac-cage recording array to thoroughly sample the cardiac potentials. With this carefully controlled experimental preparation, we minimized possible sources of error, including geometric error and torso inhomogeneities. We progressively removed recorded signals from above the atrioventricular plane to determine how the forward-computed torso-tank potentials were affected by incomplete source sampling. RESULTS: We studied 240 beats total recorded from three different activation sequence types (sinus, and posterior and anterior left-ventricular free-wall pacing) in each of two experiments. With complete sampling by the cage electrodes, all correlation metrics between computed and measured torso-tank potentials were above 0.93 (maximum 0.99). The mean root-mean-squared error across all beat types was also low, less than or equal to 0.10 mV. A precipitous drop in forward solution accuracy was observed when we included only cage measurements below the AV plane. CONCLUSION: First, our forward computed potentials using complete cardiac source measurements set a benchmark for similar studies. Second, this study validates the importance of complete cardiac source sampling above the AV plane to produce accurate forward computed torso potentials. Testing ECGI systems and techniques with these more complete and highly accurate datasets will improve inverse techniques and noninvasive detection of cardiac electrical abnormalities.

Quantifying the spatiotemporal influence of acute myocardial ischemia on volumetric conduction velocity.

INTRODUCTION: Acute myocardial ischemia occurs when coronary perfusion to the heart is inadequate, which can perturb the highly organized electrical activation of the heart and can result in adverse cardiac events including sudden cardiac death. Ischemia is known to influence the ST and repolarization phases of the ECG, but it also has a marked effect on propagation (QRS); however, studies investigating propagation during ischemia have been limited. METHODS: We estimated conduction velocity (CV) and ischemic stress prior to and throughout 20 episodes of experimentally induced ischemia in order to quantify the progression and correlation of volumetric conduction changes during ischemia. To estimate volumetric CV, we 1) reconstructed the activation wavefront; 2) calculated the elementwise gradient to approximate propagation direction; and 3) estimated conduction speed (CS) with an inverse-gradient technique. RESULTS: We found that acute ischemia induces significant conduction slowing, reducing the global median speed by 20 cm/s. We observed a biphasic response in CS (acceleration then deceleration) early in some ischemic episodes. Furthermore, we noted a high temporal correlation between ST-segment changes and CS slowing; however, when comparing these changes over space, we found only moderate correlation (corr. = 0.60). DISCUSSION: This study is the first to report volumetric CS changes (acceleration and slowing) during episodes of acute ischemia in the whole heart. We showed that while CS changes progress in a similar time course to ischemic stress (measured by ST-segment shifts), the spatial overlap is complex and variable, showing extreme conduction slowing both in and around regions experiencing severe ischemia.

Estimation and Validation of Cardiac Conduction Velocity and Wavefront Reconstruction Using Epicardial and Volumetric Data.

OBJECTIVE: In this study, we have used whole heart simulations parameterized with large animal experiments to validate three techniques (two from the literature and one novel) for estimating epicardial and volumetric conduction velocity (CV). METHODS: We used an eikonal-based simulation model to generate ground truth activation sequences with prescribed CVs. Using the sampling density achieved experimentally we examined the accuracy with which we could reconstruct the wavefront, and then examined the robustness of three CV estimation techniques to reconstruction related error. We examined a triangulation-based, inverse-gradient-based, and streamline-based techniques for estimating CV cross the surface and within the volume of the heart. RESULTS: The reconstructed activation times agreed closely with simulated values, with 50-70% of the volumetric nodes and 97-99% of the epicardial nodes were within 1 ms of the ground truth. We found close agreement between the CVs calculated using reconstructed versus ground truth activation times, with differences in the median estimated CV on the order of 3-5% volumetrically and 1-2% superficially, regardless of what technique was used. CONCLUSION: Our results indicate that the wavefront reconstruction and CV estimation techniques are accurate, allowing us to examine changes in propagation induced by experimental interventions such as acute ischemia, ectopic pacing, or drugs. SIGNIFICANCE: We implemented, validated, and compared the performance of a number of CV estimation techniques. The CV estimation techniques implemented in this study produce accurate, high-resolution CV fields that can be used to study propagation in the heart experimentally and clinically.

Uncertainty Quantification in Simulations of Myocardial Ischemia.

Computational models of myocardial ischemia are parameterized using assumptions of tissue properties and physiological values such as conductivity ratios in cardiac tissue and conductivity changes between healthy and ischemic tissues. Understanding the effect of uncertainty in these parameter selections would provide useful insight into the performance and variability of the modeling outputs. Recently developed uncertainty quantification tools allow for the application of polynomial chaos expansion uncertainty quantification to such bioelectric models in order to parsimoniously examine model response to input uncertainty. We applied uncertainty quantification to examine reconstructed extracellular potentials from the cardiac passive bidomain based on variation in the conductivity values for the ischemic tissue. We investigated the model response in both a synthetic dataset with simulated ischemic regions and a dataset with ischemic regions derived from experimental recordings. We found that extracellular longitudinal and intracellular longitudinal conductivities predominately affected simulation output, with the highest standard deviations in regions of extracellular potential elevations. We found that transverse conductivity had almost no effect on model output.

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. We applied these techniques to examine the activation sequence of the heart in response to both epicardial and endocardial stimuli within the left ventricular free wall and variations in fiber orientation. Our results showed that physiological variation in fiber orientation does not significantly impact the location of activation features, but it does impact the overall spread of activation. Future studies will investigate under which circumstances physiological changes in fiber orientation might alter electrical propagation such that the resulting simulations produce misleading outcomes.

Myocardial Ischemia Detection Using Body Surface Potential Mappings and Machine Learning.

Recent improvements in detecting acute myocardial ischemia via noninvasive body surface recordings have been driven by modern machine learning. While extensive research has been done using single and 12 lead ECGs, almost no models have incorporated body surface potential mappings. We created two contrasting machine learning models, logistic regression and XGBoost Classifier, and trained them on experimentally acquired body surface mappings with ground truth ischemia measurements recorded from within the heart. These models achieved a mean accuracy of 96.46% and 97.63%, as well as a mean AUC of 0.9927 and 0.9972 for the Logistic Regression and XGBoost classifiers, respectively. The anatomical location and relative contribution of each electrode were visualized and ranked. Then, new models were trained using data from only the top 12, 8, and 3 electrodes. These models trained on only a subset of the electrodes still exhibited relatively high accuracy and AUC, although at much faster training times.

Right Ventricular Shape Distortion in Tricuspid Regurgitation.

Tricuspid regurgitation (TR) is a failure in right-sided AV valve function which, if left untreated, leads to marked cardiac shape changes and heart failure. However, the specific right ventricular shape changes resulting from TR are unknown. The goal of this study is to characterize the RV shape changes of patients with severe TR. RVs were segmented from CINE MRI images. Using particle-based shape modeling (PSM), a dense set of homologous landmarks were placed with geometric consistency on the endocardial surface of each RV, via an entropy-based optimization of the information content of the shape model. Principal component analysis (PCA) identified the significant modes of shape variation across the population. These modes were used to create a patient-prediction model. 32 patients and 6 healthy controls were studied. The mean RV shape of TR patients demonstrated increased sphericity relative to controls, with the three most dominant modes of variation showing significant widening of the short axis of the heart, narrowing of the base at the RV outflow tract (RVOT), and blunting of the RV apex. By PCA, shape changes based on the first three modes of variation correctly identified patient vs. control hearts 86.5% of the time. The shape variation may further illuminate the mechanics of TR-induced RV failure and recovery, providing potential targets for therapies including novel devices and surgical interventions.

Novel Experimental Preparation to Assess Electrocardiographic Imaging Reconstruction Techniques.

Electrocardiographic imaging (ECGI) systems are still plagued by a myriad of controllable and uncontrollable sources of error, which makes studying and improving these systems difficult. To mitigate these errors, we developed a novel experimental preparation using a rigid pericardiac cage suspended in a torso-shaped electrolytic tank. The 256-electrode cage was designed to record signals 0.5-1.0 cm above the entire epicardial surface of an isolated heart. The cage and heart were fixed in a 192-electrode torso tank filled with electrolyte with predetermined conductivity. The resulting signals served as ground truth for ECGI performed using the boundary element method (BEM) and method of fundamental solutions (MFS) with three regularization techniques: Tikhonov zero-order (Tik0), Tikhonov second-order (Tik2), truncated singular value decomposition (TSVD). Each ECGI regularization technique reconstructed cage potentials from recorded torso potentials well with spatial correlation above 0.7, temporal correlation above 0.8, and root mean squared error values below 0.7 mV. The earliest site of activation was best identified by MFS using Tik0, which localized it to within a range of 1.9 and 4.8 cm. Our novel experimental preparation has shown unprecedented agreement with simulations and represents a new standard for ECGI validation studies.

Improving Localization of Cardiac Geometry Using ECGI.

INTRODUCTION: Electrocardiographic imaging (ECGI) requires a model of the torso, and inaccuracy in the position of the heart is a known source of error. We previously presented a method to localize the heart when body and heart surface potentials are known. The goal of this study is to extend this approach to only use body surface potentials. METHODS: We used an iterative coordinate descent optimization to estimate the positions of the heart for several consecutive heartbeats relying on the assumption that the epicardial potential sequence is the same in each beat. The method was tested with data synthesized using measurements from a isolated-heart, torso-tank preparation. Improvement was evaluated in terms of both heart localization and ECGI accuracy. RESULTS: The geometric correction resulted in cardiac geometries closely matching ground truth geometry. ECGI accuracy increased dramatically by all metrics using the corrected geometry. DISCUSSION: Future studies will employ more realistic animal models and then human subjects. Success could impact clinical ECGI by reducing errors from respiratory movement and perhaps decrease imaging requirements, reducing both cost and logistical difficulty of ECGI, widening clinical applicability.

Quantifying the Spatiotemporal Influence of Acute Myocardial Ischemia on Volumetric Conduction Speeds.

Acute myocardial ischemia compromises the ordered electrical activation of the heart, however, because of sampling limitations, volumetric changes in activation have not been measured. We used a large-animal experimental model and high-resolution volumetric mapping to study the effects of ischemia on conduction speeds (CS) throughout the myocardium. We estimated CS and electrocardiographic changes (ST segments) and evaluated the spatial and temporal correlations between them across 11 controlled episodes. We found that ischemia induces significant conduction slowing, reducing the global median speed by 25 cm/s. Furthermore, there was a high temporal correlation between the development of ischemic severity and CS (corr. = 0.93) through each episode. The spatial correlations between ST-segment changes and CS slowing were more spatially complex than expected with substantial slowing at the periphery of the zones that showed ST-segment changes. This is the first study that has documented in an experimental model volumetric changes of CS during acute myocardial ischemia and explored the relationships between ischemia development in space and time. We showed that conduction speed changes are spatiotemporally correlated to ischemic severity and illustrated the biphasic response long proposed from cellular studies.