Abstract:

Atypical atrial flutter (AFlut) is a reentrant arrhythmia which patients frequently develop after ablation for atrial fibrillation (AF). Indeed, substrate modifications during AF ablation can increase the likelihood to develop AFlut and it is clinically not feasible to reliably and sensitively test if a patient is vulnerable to AFlut. Here, we present a novel method based on personalized computational models to identify pathways along which AFlut can be sustained in an individual patient. We build a personalized model of atrial excitation propagation considering the anatomy as well as the spatial distribution of anisotropic conduction velocity and repolarization characteristics based on a combination of a priori knowledge on the population level and information derived from measurements performed in the individual patient. The fast marching scheme is employed to compute activation times for stimuli from all parts of the atria. Potential flutter pathways are then identified by tracing loops from wave front collision sites and constricting them using a geometric snake approach under consideration of the heterogeneous wavelength condition. In this way, all pathways along which AFlut can be sustained are identified. Flutter pathways can be instantiated by using an eikonal-diffusion phase extrapolation approach and a dynamic multifront fast marching simulation. In these dynamic simulations, the initial pattern eventually turns into the one driven by the dominant pathway, which is the only pathway that can be observed clinically. We assessed the sensitivity of the flutter pathway maps with respect to conduction velocity and its anisotropy. Moreover, we demonstrate the application of tailored models considering disease-specific repolarization properties (healthy, AF-remodeled, potassium channel mutations) as well as applicabiltiy on a clinical dataset. Finally, we tested how AFlut vulnerability of these substrates is modulated by exemplary antiarrhythmic drugs (amiodarone, dronedarone). Our novel method allows to assess the vulnerability of an individual patient to develop AFlut based on the personal anatomical, electrophysiological, and pharmacological characteristics. In contrast to clinical electrophysiological studies, our computational approach provides the means to identify all possible AFlut pathways and not just the currently dominant one. This allows to consider all relevant AFlut pathways when tailoring clinical ablation therapy in order to reduce the development and recurrence of AFlut.

Abstract:

Atrial fibrillation (AF) is the most prevalent form of cardiac arrhythmia. The atrial wall thickness (AWT) can potentially improve our understanding of the mechanism underlying atrial structure that drives AF and provides important clinical information. However, most existing studies for estimating AWT rely on ruler-based measurements performed on only a few selected locations in 2D or 3D using digital calipers. Only a few studies have developed automatic approaches to estimate the AWT in the left atrium, and there are currently no methods to robustly estimate the AWT of both atrial chambers. Therefore, we have developed a computational pipeline to automatically calculate the 3D AWT across bi-atrial chambers and extensively validated our pipeline on both ex vivo and in vivo human atria data. The atrial geometry was first obtained by segmenting the atrial wall from the MRIs using a novel machine learning approach. The epicardial and endocardial surfaces were then separated using a multi-planar convex hull approach to define boundary conditions, from which, a Laplace equation was solved numerically to automatically separate bi-atrial chambers. To robustly estimate the AWT in each atrial chamber, coupled partial differential equations by coupling the Laplace solution with two surface trajectory functions were formulated and solved. Our pipeline enabled the reconstruction and visualization of the 3D AWT for bi-atrial chambers with a relative error of 8% and outperformed existing algorithms by >7%. Our approach can potentially lead to improved clinical diagnosis, patient stratification, and clinical guidance during ablation treatment for patients with AF.

Abstract:

The SuLMaSS project [1] will advance, develop, build, evaluate, and test infrastructure for sustainable lifecycle management of scientific software. The infrastructure is tested and evaluated by an existing cardiac electrophysiology simulation software project, which is currently in the prototype state and will be advanced towards optimal usability and a large and active user community. Thus, SuLMaSS is focused on designing and implementing application-oriented e-research technologies and the impact is three-fold: - Provision of a high quality, user-friendly cardiac electrophysiology simulation software package that accommodates attestable needs of the scientific community. - Delivery of infrastructure components for testing, safe-keeping, referencing, and versioning of all phases of the lifecycle of scientific software. - Serve as a best practice example for sustainable scientific software management. Scientific software development in Germany and beyond shall benefit through both the aforementioned best practice role model and the advanced infrastructure that will, in part, be available for external projects as well. With adding value for the wider scientific cardiac electrophysiology community, the software will be available under an open source license and be provided for a large share of people and research groups that can potentially leverage computational cardiac modeling methods. Institutional infrastructure will be extended to explore, evaluate and establish the basis for research software development regarding testing, usage, maintenance and support. The cardiac electrophysiology simulator will drive and showcase the infrastructure formation, thus serving as a lighthouse project. The developed infrastructure can be used by other scientific software projects in future and aims to support the full research lifecycle from exploration through conclusive analysis and publication, to archival, and sharing of data and source code, thus increasing the quality of research results. Moreover it will foster a community-based collaborative development and improve sustainability of research software.

Abstract:

The heart acts as the pump of the cardiovascular system due to the active stress developed in individ- ual cardiac muscle cells. The spatio-temporal distribution of this active stress could contain relevant diagnostic information but can currently not be measured in vivo. We introduce a method to esti- mate dynamic cardiac active stress fields from endocardial surface motion tracking derived from e.g. magnetic resonance imaging data. This ill-posed non-linear problem is solved using Tikhonov regu- larization in space and time in conjunction with a continuum mechanics forward model. We present a proof-of-concept using data from a biophysically detailed multiscale model of cardiac electrome- chanics (7649 tetrahedral elements) in which we could accurately reproduce cardiac motion (surface error <0.4 mm) and identify non-contracting regions due to myocardial infarction scars (active stress error <10 kPa). This inverse method could eventually be used to non-invasively derive personalized diagnostic information in terms of dynamic active stress fields which are not accessible today.

Abstract:

Heterogeneous atrial substrate can induce, maintain and promote cardiac arrhythmias. The level of heterogeneity may be used to assess disease progression. One key parameter, suspected to be correlated with tissue vitality is the conduction velocity (CV). By measuring not only the current CV of the patient but rather its rate dependent changes, restitution information is gained. In the following, we show our approach towards a patient-specific quantitative atrial substrate characterization by combining sets of local and global CV restitution measurements to create a parametrization of the individual patient substrate characteristics.

Abstract:

Atrial fibrillation is the most common cardiac arrhythmia characterized by a rapid and irregular atrial excitation rate. Mimicking this behaviour, the S1-S2 stimulation protocol is currently the clinically established method for measuring tissue rate dependency, leading to a need for an automated segmentation method. We propose a method for stimulus artefact removal tailored towards the S1-S2 protocol. We show that this method results in the detection of atrial signals minimizing distortion by the stimulus artefact and is therefore an effective segmentation tool and a building block for automation of signal analysis.

Abstract:

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide, inducing 1/3 of all arrhythmia-related hospital admissions. The disease is described via abnormal and irregular heart rhythm and goes hand in hand with a higher risk of stroke, heart failure and even death. Multiple studies found an interrelationship, between higher age and an increased risk of AF. Moreover the total number of AF-patients is rising yearly. The disease seems stubbornly to most pharmacological and surgical attempts of treatment, which is a great danger for patients diagnosed with AF. This could be due to the fact that the pathology behind the disease still remains poorly understood. Recent research attach a high value to the concept of a single reentrant wave with fibrillatory conduction, which has an organizing centre defined as phase singularity (PS). To further understand and analyse reentrant driver dynamics throughout different arrhythmic episodes post processing algorithms were implemented and tested on three different mesh-models. A simplified slab model and two anatomically correct, volumetric, tetrahedral meshes of the human atria, whereas one atrial model has two remodelled circular areas of fibrotic tissue. PSs were detected via searching for intersection of activation (defined by a set threshold) and recovery (derivative of membrane potential equals zero) of isosurfaces. In a 3D scroll wave the organizing centre of rotation represents a line of PSs defined as filament. Because of this, neighbouring PSs, which were detected, form a filament for every screenshot of the simulation. Spatio-temporal behaviour of detected filaments was considered by searching for intersection of filaments for the current time t and filaments detected in t+1. The path of each filament was tracked, leading to results which show filament dynamics. Because of many short living filaments the outcome was filtered by a minimum lifetime, leading to results which show only the important drivers for our study. To further investigate the area in which the driver seemed to be stable, an algorithm finding the point of highest probability for a PS to meander through this area, was developed. This point was defined as the area on the epicardium the PS passes by most often throughout the simulation, for one single reentrant driver. We assumed that this point of highest probability for finding a PS would lie in the centre of the rotation. Wherefore we detected the distance from the point of highest probability of finding PS to a point furthest away still belonging to the same rotation. This distance should represent an approximate radius of the rotation and include all mesh elements belonging to the rotation. The results of this thesis indicated that many short living filaments accrued because of wave collisions, visible throughout the simulation. Furthermore initiated drivers have been wiped out via the fibrotic tissue and no stable rotors were detected in this case, but the arrhythmia was still ongoing. Which means a single persistent rotor could not be the only reason for AF maintenance. All in all this study provides new methods to analyse the dynamics of reentrant drivers throughout different arrhythmic episodes. Especially the detection of the point of highest PS probability could lead to further studies to analyse the influence of fibrotic tissue applied to stable reentrant drivers in a preferably small area and the resulting influence to the arrhythmia.