Image quality assessment and automation in late gadolinium-enhanced MRI of the left atrium in atrial fibrillation patients.

Background: Late gadolinium-enhanced (LGE) MRI has become a widely used technique to non-invasively image the left atrium prior to catheter ablation. However, LGE-MRI images are prone to variable image quality, with quality metrics that do not necessarily correlate to the image's diagnostic quality. In this study, we aimed to define consistent clinically relevant metrics for image and diagnostic quality in 3D LGE-MRI images of the left atrium, have multiple observers assess LGE-MRI image quality to identify key features that measure quality and intra/inter-observer variabilities, and train and test a CNN to assess image quality automatically.

Functional and Structural Remodeling as Atrial Fibrillation Progresses in a Persistent Atrial Fibrillation Canine Model.

Background: Contractile, electrical, and structural remodeling has been associated with atrial fibrillation (AF), but the progression of functional and structural changes as AF sustains has not been previously evaluated serially.

Objectives: Using a rapid-paced persistent AF canine model, the authors aimed to evaluate the structural and functional changes serially as AF progresses.

Methods: Serial electrophysiological studies in a chronic rapid-paced canine model (n = 19) prior to AF sustaining and repeated at 1, 3, and 6 months of sustained AF were conducted to measure changes in atrial conduction speed and direction.

Effective ablation settings that predict chronic scar after atrial ablation with HELIOSTAR™ multi-electrode radiofrequency balloon catheter.

Background: Radiofrequency balloon (RFB) ablation (HELIOSTAR™, Biosense Webster) has been developed to improve pulmonary vein ablation efficiency over traditional point-by-point RF ablation approaches. We aimed to find effective parameters for RFB ablation that result in chronic scar verified by late gadolinium enhancement cardiac magnetic resonance (LGE-CMR).

Methods: A chronic canine model (n = 8) was used to ablate in the superior vena cava (SVC), the right superior and the left inferior pulmonary vein (RSPV and LIPV), and the left atrial appendage (LAA) with a circumferential ablation approach (RF energy was delivered to all electrodes simultaneously) for 20 s or 60 s.

Diffuse functional and structural abnormalities in fibrosis: Potential structural basis for sustaining atrial fibrillation.

Background: Structural remodeling has been associated with increased incidence of atrial fibrillation, but how fibrotic regions allow atrial fibrillation to be sustained remains unclear.

Objective: With a novel transgenic goat model, we evaluated structural and functional differences between structurally remodeled and healthy regions of the atria.

Methods: A novel transgenic goat model with cardiac-specific overexpression of transforming growth factor β1 was used.

Performance of off-the-shelf machine learning architectures and biases in low left ventricular ejection fraction detection.

Background: Artificial intelligence-machine learning (AI-ML) has demonstrated the ability to extract clinically useful information from electrocardiograms (ECGs) not available using traditional interpretation methods. There exists an extensive body of AI-ML research in fields outside of cardiology including several open-source AI-ML architectures that can be translated to new problems in an "off-the-shelf" manner.

Objective: We sought to address the limited investigation of which if any of these off-the-shelf architectures could be useful in ECG analysis as well as how and when these AI-ML approaches fail.

Left atrial functional changes associated with repeated catheter ablations for atrial fibrillation.

Introduction: The impact of repeated atrial fibrillation (AF) ablations on left atrial (LA) mechanical function remains uncertain, with limited long-term follow-up data.

Methods: This retrospective study involved 108 AF patients who underwent two catheter ablations with cardiac magnetic resonance imaging (MRI) done before and 3 months after each of the ablations from 2010 to 2021. The rate of change in peak longitudinal atrial strain (PLAS) assessed LA function.

Uncertainty Quantification of the Effect of Variable Conductivity in Ventricular Fibrotic Regions on Ventricular Tachycardia.

Ventricular tachycardia (VT) is a life-threatening cardiac arrhythmia for which a common treatment pathway is electroanatomical mapping and ablation. Recent advances in both noninvasive ablation techniques and computational modeling have motivated the development of patient-specific computational models of VT. Such models are parameterized by a wide range of inputs, each of which is associated with an often unknown amount of error and uncertainty.

Uncertainty Quantification of Fiber Orientation and Epicardial Activation.

Predictive models and simulations of cardiac function require accurate representations of anatomy, often to the scale of local myocardial fiber structure. However, acquiring this information in a patient-specific manner is challenging. Moreover, the impact of physiological variability in fiber orientation on simulations of cardiac activation is poorly understood.

Capturing the Influence of Conduction Velocity on Epicardial Activation Patterns Using Uncertainty Quantification.

Individual variability in parameter settings, due to either user selection or disease states, can impact accuracy when simulating the electrical behavior of the heart. Here, we aim to test the impact of inevitable uncertainty in conduction velocities (CVs) on the output of simulations of cardiac propagation, given three stimulus locations on the left ventricular (LV) free wall. To understand the role of physiological variability in CV in simulations of cardiac activation, we generated detailed maps of the variability in propagation simulations by implementing bi-ventricular activation simulations and quantified the effects by deploying robust uncertainty quantification techniques based on polynomial chaos expansion (PCE).

Ablation-induced left atrial mechanical dysfunction recovers in weeks after ablation.

Background: The immediate impact of catheter ablation on left atrial mechanical function and the timeline for its recovery in patients undergoing ablation for atrial fibrillation (AF) remain uncertain. The mechanical function response to catheter ablation in patients with different AF types is poorly understood.

Methods: A total of 113 AF patients were included in this retrospective study.

Effects of Biventricular Pacing Locations on Anti-Tachycardia Pacing Success in a Patient-Specific Model.

Patients with drug-refractory ventricular tachycardia (VT) often undergo implantation of a cardiac defibrillator (ICD). While life-saving, shock from an ICD can be traumatic. To combat the need for defibrillation, ICDs come equipped with low-energy pacing protocols.

Interactions between calcium-induced arrhythmia triggers and the electrophysiological-anatomical substrate underlying the induction of atrial fibrillation.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is sustained by spontaneous focal excitations and re-entry. Spontaneous electrical firing in the pulmonary vein (PV) sleeves is implicated in AF generation. The aim of this simulation study was to identify the mechanisms determining the localisation of AF triggers in the PVs and their contribution to the genesis of AF.

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.

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.

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

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).

Investigation into the importance of using natural PVCs and pathological models for potential-based ECGI validation.

Premature ventricular contractions (PVCs) are one of the most commonly targeted pathologies for ECGI validation, often through ventricular stimulation to mimic the ectopic beat. However, it remains unclear if such stimulated beats faithfully reproduce spontaneously occurring PVCs, particularly in the case of the R-on-T phenomenon. The objective of this study was to determine the differences in ECGI accuracy when reconstructing spontaneous PVCs as compared to ventricular-stimulated beats and to explore the impact of pathophysiological perturbation on this reconstruction accuracy.

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.

Contribution of atrial myofiber architecture to atrial fibrillation.

Background: The role of fiber orientation on a global chamber level in sustaining atrial fibrillation (AF) is unknown. The goal of this study was to correlate the fiber direction derived from Diffusion Tensor Imaging (DTI) with AF inducibility.

Methods: Transgenic goats with cardiac-specific overexpression of constitutively active TGF-β1 (n = 14) underwent AF inducibility testing by rapid pacing in the left atrium.

UncertainSCI: Uncertainty quantification for computational models in biomedicine and bioengineering.

Background: Computational biomedical simulations frequently contain parameters that model physical features, material coefficients, and physiological effects, whose values are typically assumed known a priori. Understanding the effect of variability in those assumed values is currently a topic of great interest. A general-purpose software tool that quantifies how variation in these parameters affects model outputs is not broadly available in biomedicine.

Improving Generalization by Learning Geometry-Dependent and Physics-Based Reconstruction of Image Sequences.

Deep neural networks have shown promise in image reconstruction tasks, although often on the premise of large amounts of training data. In this paper, we present a new approach to exploit the geometry and physics underlying electrocardiographic imaging (ECGI) to learn efficiently with a relatively small dataset. We first introduce a non-Euclidean encoding-decoding network that allows us to describe the unknown and measurement variables over their respective geometrical domains.

Case report: Personalized computational model guided ablation for left atrial flutter.

Atypical atrial flutter is seen post-ablation in patients, and it can be challenging to map. These flutters are typically set up around areas of scar in the left atrium. MRI can reliably identify left atrial scar.

The Effect of Segmentation Variability in Forward ECG Simulation.

Segmentation of patient-specific anatomical models is one of the first steps in Electrocardiographic imaging (ECGI). However, the effect of segmentation variability on ECGI remains unexplored. In this study, we assess the effect of heart segmentation variability on ECG simulation.

Effect of Segmentation Uncertainty on the ECGI Inverse Problem Solution and Source Localization.

Electrocardiographic Imaging (ECGI) is a promising tool to non-invasively map the electrical activity of the heart using body surface potentials (BSPs) and the patient specific anatomical data. One of the first steps of ECGI is the segmentation of the heart and torso geometries. In the clinical practice, the segmentation procedure is not fully-automated yet and is in consequence operator-dependent.